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RetroDepth: 3D Silhouette Sensing for High Precision Input On and Above Physical Surfaces

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We present RetroDepth, a new vision-based system for accurately sensing the 3D silhouettes of hands, styluses, and other objects, as they interact on and above physical surfaces. Our setup is simple, cheap, and easily reproducible, comprising of two infrared cameras, diffuse infrared LEDs, and any off-the-shelf retro-reflective material. The retro-reflector aids image segmentation, creating a strong contrast between the surface and any object in proximity. A new highly efficient stereo matching algorithm precisely estimates the 3D contours of interacting objects and the retro-reflective surfaces. A novel pipeline enables 3D finger, hand and object tracking, as well as gesture recognition, purely using these 3D contours. We demonstrate high-precision sensing, allowing robust disambiguation between a finger or stylus touching, pressing or interacting above the surface. This allows many interactive scenarios that seamlessly mix together freehand 3D interactions with touch, pressure and stylus input. As shown, these rich modalities of input are enabled on and above any retro-reflective surface, including custom "physical widgets" fabricated by users. We compare our system with Kinect and Leap Motion, and conclude with limitations and future work.
Left: contour-based depth estimation for rectified images. For notation please see text. Right: Cost Matrix C. The brightness of each cell indicates the accumulated costs at C(i, j) (dark cells have low costs and bright ones high costs). The path of lowest costs is indicated in red. Matching pixels are marked with a red circle. All other pixels along the red path are occluded in the left or right image. Let p and q be contour points in the left and right image, respectively. P and Q are lists of length |P | and |Q| that store the image x-coordinate of those contour points lying on scanline S in the left and right image, respectively. P (i) denotes the i th element in the list and we assume that the lists P and Q are sorted. For better understanding, Fig. 5 illustrates rectified stereo images that show a contour as well as lists P and Q for scanline S. To find correspondences between the points in P and Q, we compute the minimum-cost path through a matrix C, illustrated in Fig. 5 (right). C is of size |P | × |Q| and C(i, j) stores the minimum accumulated cost of an optimal path from (0, 0) to (i, j). The path of minimum cost is indicated in red in Fig. 5. Every path is restricted to start at cell (0, 0) and to end at cell (|P |, |Q|), because we require a mapping for all contour points. Furthermore, only three moves are possible to construct a path: a diagonal 45 • move that indicates a match, as well as horizontal and vertical moves that represent points that are only visible in the left or right image, respectively. The restrictions imposed on the path imply the following properties of the solution: Uniqueness property: Every (contour) point in P can only match to one point in Q and vice versa. Ordering property: If point P (i) matches Q(j) than P (i + 1) can only match to Q(j + ) where > 0.
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RetroDepth: 3D Silhouette Sensing for High-Precision
Input On and Above Physical Surfaces
David Kim1,3, Shahram Izadi1, Jakub Dostal4, Christoph Rhemann1, Cem Keskin1
Christopher Zach1, Jamie Shotton1, Tim Large2, Steven Bathiche2
Matthias Nießner5, D. Alex Butler1, Sean Fanello6, Vivek Pradeep2
1Microsoft Research 2Microsoft 3Newcastle University
4University of St. Andrews 5Stanford University 6Italian Institute of Technology
Figure 1: RetroDepth is a new 3D silhouette sensing system for high-precision interaction on and above physical surfaces.
ABSTRACT
We present RetroDepth, a new vision-based system for ac-
curately sensing the 3D silhouettes of hands, styluses, and
other objects, as they interact on and above physical surfaces.
Our setup is simple, cheap, and easily reproducible, compris-
ing of two infrared cameras, diffuse infrared LEDs, and any
off-the-shelf retro-reflective material. The retro-reflector aids
image segmentation, creating a strong contrast between the
surface and any object in proximity. A new highly efficient
stereo matching algorithm precisely estimates the 3D con-
tours of interacting objects and the retro-reflective surfaces.
A novel pipeline enables 3D finger, hand and object track-
ing, as well as gesture recognition, purely using these 3D
contours. We demonstrate high-precision sensing, allowing
robust disambiguation between a finger or stylus touching,
pressing or interacting above the surface. This allows many in-
teractive scenarios that seamlessly mix together freehand 3D
interactions with touch, pressure and stylus input. As shown,
these rich modalities of input are enabled on and above any
retro-reflective surface, including custom “physical widgets”
fabricated by users. We compare our system with Kinect and
Leap Motion, and conclude with limitations and future work.
Author Keywords
NUI, 3D contours, depth sensing, 3D input, touch, stylus,
vision-based UIs, stereo matching, contour classification
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http://dx.doi.org/10.1145/2556288.2557336
INTRODUCTION
Natural user interfaces (NUI) have received much attention
recently, as they provide rich modalities and possibilities for
interaction, beyond mouse and keyboard. Whilst the term is
broad and can encompass touch, gesture, gaze, voice, and
tangible input, NUI often implies leveraging the dexterity and
higher degrees-of-freedom (DoF) of our hands for interaction.
However, even when focusing on hands, NUI has vastly dif-
ferent interpretations. A decade ago, the term predominately
referred to emerging multi-touch interfaces. More recently, it
has become more synonymous with 3D touchless input using
fingers, hands or whole body interactions, popularized by
products such as Kinect and Leap Motion.
This has resulted in the emergence of many underlying NUI
technologies designed with very different scenarios in mind,
each with strengths and weaknesses. For example, Leap
Motion is only able to estimate fingertips of a hand in-air,
but at a high precision, where Kinect provides more flexibility
in sensing dense depth maps of arbitrary scenes and tracking
a human skeleton, but at a relatively low precision.
In this paper we present RetroDepth, a system that unifies a
variety of NUI modalities such as freehand touch, pressure,
in-air interactions, as well as stylus and other tangible ob-
ject input. Our system is designed specifically for scenarios
where users interact on and above physical surfaces of differ-
ent shapes and sizes, and allows seamless switching between
these modes of input. RetroDepth provides a middle ground
between sensors that extract high-level features such as finger-
tips (e.g. Leap Motion) and more flexible depth cameras (e.g.
Kinect) that expose raw depth maps but at lower precision.
Our system is designed specifically to sense only the 3D sil-
houette of interacting objects. The use of silhouettes greatly
lowers the computation required for depth sensing, when com-
pared to estimating a dense depth map. We present a novel
Session: On and Above the Surface
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vision-based approach for silhouette sensing, which leads to
extremely high-precision contour estimation, and demonstrate
sufficient accuracy to distinguish between touch, hover and
pressure. Interestingly, most consumer depth cameras, such
as stereo cameras, Kinect or even time-of-flight sensors (ToF),
struggle the most at object boundaries and edges, where large
depth discontinuities lead to noise and imprecise depth estima-
tion. In this paper, we highlight the importance of precision
at these object edges, as ultimately these form the main point
of contact between the user and the digital/physical world.
We highlight numerous examples of how the resulting 3D sil-
houette information can be exploited for interactive scenarios.
In particular, we demonstrate just how much information a
silhouette can encode, and present a novel pipeline that identi-
fies and tracks fingers and objects in 3D, and infers hand parts,
poses and gestures, using 3D contours only. We compare the
accuracy of our system with Kinect and Leap Motion, and
conclude with a discussion of limitations and future work.
RELATED WORK
Early uses of 2D silhouettes and contours in the context of
interaction coincide with pioneering work on NUI, indicating
their importance for enabling such experiences. Krueger’s
seminal work [
21
] and SideShow [
9
] enables whole-body and
gestural interactions using 2D silhouettes. In recent years, 2D
silhouettes and contours have been exploited in the context of
vision-based tabletop systems [39, 28, 37, 20, 41]
Top-down configurations that place a 2D camera above a
physical surface have proved popular as they provide more
flexibility in sensing, allowing any regular desk to be trans-
formed into an interactive one. Because these systems rely on
2D cameras, segmentation of hands and objects from the (typ-
ically cluttered) desktop is non-trivial, and touch and hover
is difficult to disambiguate. These challenges have led re-
searchers to use stereo cameras for higher precision touch
sensing, combined with simple “hardware” techniques for
segmentation, e.g. colored backgrounds or polarizing filters
[
25
,
2
]. Izadi et al. [
16
] use the same configuration as [
2
]
to combine stereo-based touch estimation with stylus input,
object recognition, and depth sensing, but the latter is used
for remote feedback only.
Two recent notable products enable high-precision sensing
using stereo cameras. The commercial Leap Motion sensor
is a small, self-contained unit containing a pair of wide field-
of-view (FoV) infrared (IR) cameras and IR LEDs pointing
upwards on a desk. Whilst not published, the device provides
tracking of fingertips and other strong peaks in the input signal,
such as a pen, with high frame-rate and precision sensing. The
device does not expose a full contour, only tracked 3D points
mapping to high-level features such as fingertips. Further,
segmentation is based on assuming that only freespace exists
behind interacting objects. This limits the device to in-air
input only, which can result in arm fatigue during prolonged
interactions. Haptix [
11
] uses a similar small desktop unit
with two IR cameras but instead points across the physical
surface to detect multi-touch gestures only. Our approach
combines these modalities of high-precision touch and in-air
input, but also provides features such as pressure sensing, full
contour-based interactions, gestures, and tracking of hands
and tangible objects.
Going beyond the tabletop
A logical next step for interac-
tive surfaces is to explore in-air interactions, potentially in
combination with touch. Systems have demonstrated stylus-
based input using magnetic, inductive or vision-based sys-
tems (see [
35
,
40
]). Many optical and non-optical techniques
have been explored for freehand in-air input in the context
of interactive surfaces. These include electric-field sensing
techniques [
30
,
23
], use of scanning lasers [
30
] and laser-line
generators [
36
], or IR proximity sensors [
29
,
17
,
4
], either
placed around the bezel or embedded behind a display. These
techniques either coarsely sense 3D input, or have a limited
interaction volume or are limited to 3D input only.
Other notable systems combine on-surface and in-air interac-
tions using a variety of different hardware configurations. For
example, the use of high-end (and costly) marker-based mo-
tion trackers used for 3D tabletops [
26
] or even 3D cardboard
or foam prototypes [
3
]; as well as, synchronized IR cameras
and switchable diffuser to image on and beyond the surface
[
18
], where depth was coarsely estimated using diffuse IR
light intensity falloff [
13
]; and light field imaging through
an LCD to coarsely estimate depth [
15
]. Prior to the wide
availability of depth cameras, researchers also explored ToF
sensors placed behind [
13
] and above [
43
] projection surfaces.
These sensors suffered from low resolution and high noise
making interactions coarse and limited.
The rise of consumer depth cameras
With the advent of
consumer depth cameras, many new systems for in-air interac-
tions coupled with surface-based interactions have appeared.
Examples include situated augmented reality displays such
as [
14
,
5
], as well as augmented desktops [
12
,
24
]. All these
systems use a top-down Kinect camera to both sense touch
[
44
] and in-air interactions. These systems inherently lack
precision due to the quantization noise of the Kinect (see be-
low) making such systems imprecise compared to other input
devices [
10
]. 3Gear Systems [
1
] demonstrate tracking of spe-
cific hand gestures above a desktop, again using a top-down
Kinect camera, based on early research of Wang et al. [
38
].
This method can be considered state-of-the-art in terms of
Kinect-based hand tracking, and we provide quantitative com-
parisons later in this paper.
One of the main drawbacks with consumer depth cameras
is high noise at object boundaries. However, for NUI, these
boundaries are critical for sensing when the user’s hands (or
other physical tool) interacts with either digital or physical
content. The original Kinect uses a method akin to stereo
matching, where each small 2D patch of the reference dot
pattern is matched by sweeping a window along the associ-
ated epipolar line in the observed IR image. This leads to
ambiguities when matching at object boundaries, where large
depth discontinuities lead to outliers, holes, and edge fatten-
ing [
32
,
8
]. ToF sensors suffer from issues such as multi-path
or mixed pixels [
31
], where light returns from secondary sur-
faces to the same pixel during a single exposure, as well as
flying pixels’ where depth is estimated across multiple cap-
tures and scene motion results in foreground and background
contributions being averaged together [31].
Custom depth cameras
This fundamental issue of con-
sumer depth cameras is a key motivation for RetroDepth,
where we specifically design a system for real-time, high-
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precision depth estimation at silhouette and object boundaries.
Whilst consumer depth cameras lack precision, researchers
have demonstrated much higher quality sensors, in particu-
lar by using dynamic structured light patterns (see [
22
] for
a review). There are however limitations in such systems
for our interactive scenarios. Most of these systems estimate
depth by projecting multiple dynamic patterns into the scene
and capturing multiple images with a camera. To deal with a
moving scene (which is necessary in our interactive scenar-
ios) and therefore limit motion artifacts, these patterns need
to be projected and imaged at very high frame-rates. This
necessitates costly high speed camera and projector hardware
that can add considerably to the system cost. There is also the
computational cost associated with estimating dense depth
maps, which as demonstrated in this paper is unnecessary for
many interactive scenarios.
RetroDepth
In the following sections we present the physical RetroDepth
setup and demonstrate the interactive capabilities of the sys-
tem. The physical configuration is shown in Fig. 1. Two
off-the-shelf monochrome cameras are positioned on a metal
rail at a fixed baseline (6cm apart). Currently we use Lumen-
era PLT-425-NIR (running at 1024x512 and 60Hz) due to
their IR response and global shutter capabilities, but there is
no hard restriction on manufacturer or imaging sensor. A ring
of 8 diffuse IR LEDs (OSRAM CHIPLED SFH 4053) oper-
ating at 850nm are attached around the lens of each camera.
This placement and number of LEDs ensures that illumination
is uniform and shadows are minimized. IR bandpass filters
are placed on each camera lens.
This hardware setup is easily reproducible with only off-the-
shelf components. The setup is powered entirely over USB
(peak power for each LED is 260mW, and average power is
35mW). The LEDs are pulsed from each camera’s General
Purpose Input/Output (GPIO) pins, and only active during the
camera exposure (2ms). A synchronization board is used to
generate a 60Hz trigger to shutter the cameras simultaneously.
Figure 2: Left: passive retro-reflective keyboard, and series
of other “physical widgets”. Right: IR image of scene.
Retro-reflective segmentation
Fig. 2 also shows retro-reflective surfaces (such as the off-
the-shelf 3M Scotchlite-Part 8910), which are cut in various
shapes and sizes, and placed on a table. Upon shuttering,
the cameras both simultaneously capture an image of the
scene. All IR LEDs are switched on during this exposure
time, producing a bright uniform response from all the retro-
reflective materials on the table, as shown in Fig. 2 (right).
This makes these reflective surfaces readily distinguishable
as bright silhouettes in the captured stereo images. When
objects interact on or above these surfaces, a sharp contrast
is created between the background and foreground, e.g. the
hand or marker pen shown in Fig. 2 (right).
In a first pass, we extract contours of any bright silhouettes
observed from each camera. We then perform stereo matching
across these contour images. In a second pass, we invert the
image within the contour boundaries, creating bright silhou-
ettes within each retro-reflective region, which correspond
to objects (e.g. hands or styluses), interacting on or above
the surface (Fig. 2). We again extract contours and perform
stereo matching in these regions. This creates a clean separa-
tion between sensing the retro-reflective objects, and the 3D
interactions occurring on or above them. Optionally, we inter-
polate to fill in a dense 3D silhouette of foreground objects.
Creating and tracking physical widgets
Retro-reflective material is cheap and readily procurable. It is
also easy to cut into various shapes and sizes. Fig. 2 shows a
variety of simple “physical widgets”, made by cutting sheets
of acrylic or cardboard into the desired shape and gluing the
retro-reflective material on the upper-side. Additionally, a
clear acetate sheet is used to print labels (most printer ink is
invisible to IR) for the widget, such as keys for a 3D sensing
touch keyboard (Fig. 2, left).
Our stereo matching algorithm estimates the precise metric
depth of the outlines of these physical widgets. After depth
estimation, a machine learning algorithm classifies the widget
based on silhouette shape. This classification occurs per frame
in real-time and is robust to large parts of the widget being
occluded by interacting hands and objects. Once classified
the widget can be tracked in 6DoF. Given that new physical
widgets are easy to fabricate, our machine learning algorithm
is extensible in order to add new training classes at runtime.
Freehand 3D interactions and gestures
As the user’s hand or physical tool interacts above a retro-
reflective widget, a clear internal silhouette is visible (Fig. 2,
right). By inverting the image, our stereo matching algorithm
can precisely estimate the depth of these internal silhouettes.
Even a small occluding region of the finger can be sensed
accurately. This creates an interaction volume above each
widget that allows freehand 3D input. Depending on the size
of widget this can be either bimanual or single hand input,
with fine-scale sensing of individual fingers (Fig. 3).
Figure 3: Left: Recovering the precise 3D contour of ob-
jects placed on the retro-reflector, including hands and other
physical objects. Middle: Distinguishing between the wid-
get (red contour) and the interacting objects (blue contour).
Middle & Right: Sensing in-air (green), touch (red) and
pressure (blue) input.
We repurpose the same machine learning pipeline to detect
distinct hand shapes (i.e. when performing different gestures).
Our system currently identifies a number of shapes includ-
ing pointing, pinching, and whole hand interactions. Our
approach can robustly handle variations across poses, and
also estimate whether the left or right hand is observed.
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After classifying the hand into distinct shapes, a machine
learning classifier robustly identifies whether contour points
belong to fingertips or the thumb, based on a curvature met-
ric. These classified contour points can be clustered, and the
individual fingertip peak identified. Simple line-fitting can
be used to determine orientation of the fingertip in 3D. As
shown in Fig. 1, Fig. 3 and accompanying video this allows
for robust real-time identification of the digits of the hand.
Stylus input and tangible tools
As shown in Fig. 3 and Fig. 4 a variety of objects placed on
top of a retro-reflective surface can be clearly identified by
their contours. These objects could either invoke specific UI
commands, or be used for continuous input. One example
of the latter is a stylus, which can be used for fine grained
manipulations in 3D as shown in Fig. 1 and accompanying
video. During contour extraction, we look for very sharp
peaks in the signal to identify the pen tip, and estimate the
pen location and orientation in 3D (using line fitting).
Touch & Pressure
Beyond the 3D and tangible interactions outlined above, each
retro-reflective widget has the capability of supporting touch
and pressure input purely with our depth estimation method.
To detect when finger or stylus touches a flat surface, a plane
is estimated based on the widget’s contour and intersections
with any classified tips identified. One interesting possibility
as shown in the accompanying video and Fig. 3 is to use
a malleable material for the widget allowing users to press
fingers into the substrate. Here the precision of our contour-
based depth estimation method allows very small changes in
the 3D location of the fingertip to be identified when the user
presses the surface to approximate pressure.
Example physical widgets
Fig. 2 shows a variety of different widgets that take minutes to
fabricate. As highlighted just by virtue of covering an object
with retro-reflective material, a interactive volume is created
above the object, where touch, pressure, in-air and stylus
input can be sensed readily, without any direct computational
augmentation of the widget. Widgets that allow for 3D touch
pads, mice, and keyboards can be easily created. Sheets of
retro-reflector can be rolled out to enable ad-hoc 3D input
devices. Physical toolbars and marking menus can be mapped
to specific 2D UI functions, e.g. color selection.
SOFTWARE PIPELINE
So far we have provided a high level view of the RetroDepth
system and its sensing and interactive capabilities. In the
next sections, we describe the software processing pipeline
that takes pairs of synchronized images and estimates depth,
and classifies widgets, hand shapes and salient hand features.
The pipeline is composed of fairly standard image processing
tasks, followed by a new contour-based stereo and classifica-
tion algorithm. We briefly discuss these initial steps before
focusing on the novel components.
First an offline calibration process is performed which com-
prises of: 1) intrinsic calibration to compute the geometric
parameters of each IR camera lens (focal length, principal
point, radial and tangential distortion); 2) stereo calibration to
compute the geometric relationship between the two cameras,
expressed as a rotation matrix and translation vector; 3) stereo
rectification to correct the camera image planes to ensure they
are scanline-aligned to simplify disparity computation. At
runtime, the synchronized input IR images are undistorted
given intrinsic lens parameters, scanline rectified, and finally
cropped to ignore non-overlapping parts.
Figure 4: Early stages of processing pipeline. Top left:
scanline-rectified stereo images. Bottom left: Foreground
segmentation (in red) of internal objects within convex hull
(blue). Note convex hull maps to each physical widget. Top
right: extracted foreground contours. Bottom right: esti-
mated depth of foreground silhouette (shown in false color).
The remainder of this online pipeline works in two steps. First,
the depth for the background objects is computed, i.e. the
visible widgets that are brightly lit. Second, depth is estimated
for foreground objects, i.e. internal silhouettes within each
widget. Because of the sparse nature of the contour-based
algorithm, we can efficiently compute depth in two-passes.
To compute depth for background objects, we threshold both
scanline-rectified images to remove low intensity pixels. We
trace the contour of each binary image, which efficiently com-
putes a set of 2D pixel coordinates corresponding to contour
points for each connected component. At this point, stereo is
computed on these background contours to estimate the 3D
silhouettes of the widgets.
What remains is to compute the depth for internal foreground
physical objects such as hands (the dark silhouettes inside the
widget). We compute the convex hull for each background
object, and generate a binary mask (1 within the convex hull,
and 0 outside) for both stereo images. For each valid pixel
in the mask, we invert the binary image, and repeat contour
extraction on these points. This leaves us with two pairs of
contour images; one corresponding to bright regions in the
input images, and the other the silhouettes (of foreground
objects) within these bright regions. Finally, our efficient
stereo matching algorithm is computed for the foreground.
(Fig. 4) shows an example of this processing pipeline.
Efficient Contour-based Stereo
Stereo algorithms identify corresponding points in both im-
ages that are projections from the same scene point. We refer
to [
32
,
8
] for an in-depth discussion of stereo matching ap-
proaches. Because our input images are rectified, correspond-
ing points are known to lie on the same horizontal scanline in
the left and right image (see Fig. 5), which reduces the depth
estimation to a 1D search task. The horizontal displacement
is known as disparity
d
and is inversely proportional to depth.
In the following, we focus on the key problem of determin-
ing the correspondences between contour points that lie on a
particular scanline S.
A core contribution of our work is extending the standard
dynamic programming dense stereo algorithms to sparse con-
tours, which greatly reduces the amount of computation,
whilst increasing precision of disparity estimation.
Session: On and Above the Surface
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1380
Figure 5: Left: contour-based depth estimation for rectified
images. For notation please see text. Right: Cost Matrix
C. The brightness of each cell indicates the accumulated
costs at C(i, j)(dark cells have low costs and bright ones
high costs). The path of lowest costs is indicated in red.
Matching pixels are marked with a red circle. All other pixels
along the red path are occluded in the left or right image.
Let
p
and
q
be contour points in the left and right image,
respectively.
P
and
Q
are lists of length
|P|
and
|Q|
that
store the image
x
-coordinate of those contour points lying
on scanline
S
in the left and right image, respectively.
P(i)
denotes the
ith
element in the list and we assume that the
lists
P
and
Q
are sorted. For better understanding, Fig. 5
illustrates rectified stereo images that show a contour as well
as lists Pand Qfor scanline S.
To find correspondences between the points in
P
and
Q
, we
compute the minimum-cost path through a matrix
C
, illus-
trated in Fig. 5 (right).
C
is of size
|P|×|Q|
and
C(i, j)
stores
the minimum accumulated cost of an optimal path from
(0,0)
to
(i, j)
. The path of minimum cost is indicated in red in
Fig. 5. Every path is restricted to start at cell
(0,0)
and to
end at cell
(|P|,|Q|)
, because we require a mapping for all
contour points. Furthermore, only three moves are possible to
construct a path: a diagonal
45
move that indicates a match,
as well as horizontal and vertical moves that represent points
that are only visible in the left or right image, respectively.
The restrictions imposed on the path imply the following
properties of the solution:
Uniqueness property:
Every (contour) point in
P
can only
match to one point in
Q
and vice versa.
Ordering property:
If point
P(i)
matches
Q(j)
than
P(i+ 1)
can only match to
Q(j+4)where 4>0.
We enforce a third constraint, which we empirically found
beneficial to improve robustness: Let
K
be the contour that
P(i)
belongs to and
K0
the contour that
Q(j)
belongs to.
Then if
P(i)
matches
Q(j)
the height of the bounding boxes
of Kand K0must not differ by more than 50 pixels.
Energy Function
The (accumulated) cost of a path is defined recursively (for
i > 0and j > 0) as:
C(i, j) = min
C(i1, j 1) + Cmatch(i, j ) + Csmooth(i, j)
C(i1, j) + λo cclusion
C(i, j 1) + λocclusion
(1)
where the three different cases correspond to the three permit-
ted moves discussed above. The boundary conditions are
C(0,0) = Cmatch(0,0)
C(i, 0) = i·λocclusion i > 0
C(0, j) = j·λo cclusion j > 0
In Eq. 1,
λocclusion
is a constant occlusion penalty and the
remaining terms are defined as follows.
The data term
Cmatch
measures the compatibility of putatively
matching contour points:
Cmatch(i, j) = k~
n(i)~
n(j)k
+|distC(P(i)) distC(Q(j))|.(2)
Here, the first part measures the Euclidean distance of the
normal vectors
~
n(i)
and
~
n(j)
at contour points indexed by
P(i)
and
Q(j)
, respectively. The second part compares
the horizontal distance of point
P(i)
and
Q(j)
to the cen-
troid of their corresponding contours
K
and
K0
, respectively:
distC(P(i)) = P(i)Centroid(K).
The pairwise term
Esmooth
encourages solutions where the
horizontal distance between two neighboring matching points
P(i)
and
P(ϕ(i))
is similar to the distance of their matching
points Q(j)and Q(l):
Esmooth(i, j ) = kP(i)P(ϕ(i))(Q(j)Q(ϕ(j)))k,(3)
where function
ϕ()
returns the closest previous matching
point for the current path.
Optimization via Dynamic Programming
Dynamic programming is a well established technique to find
the path of minimal cost through
C
. The optimization consists
of two consecutive steps. First, the cumulative costs
C(i, j)
are computed recursively for every pair
(i, j)
as per Eq. 1.
At each recursion we also store the best “move” (i.e. the
arg min
of Eq. 1) at
(i, j)
in a matrix
M
that has the same
dimensions as
C
. In the second step we reconstruct the best
path by tracing back the best moves stored in
M
starting from
(|P|,|Q|)
until we reach the origin
(0,0)
of
M
. Once the best
path is computed the disparity
d(i)
(with respect to the left
image) can be derived as
d(i) = P(i)Q(j)
for matching
points P(i)and P(j).
Post Processing
Although the estimated disparity map is usually of high qual-
ity, there might be outliers and contour points where no depth
could be estimated (e.g. due to occlusion of this point in the
other image). In the post-processing stage we aim to filter out
wrong matches and assign all points along the contour to a
depth value. In particular we apply the following steps:
Invalidation of horizontal lines.
The depth estimation can
be unreliable at contour points whose normal is almost per-
pendicular to the scanlines (such regions are marked in orange
in Fig. 5). This is because the term
Cmatch
is ambiguous in
those regions. Therefore, we invalidate contour points whose
normal vector has an angle of 85.
Outlier invalidation.
The depth map obtained with dynamic
programming is computed independently for each scanline.
As a consequence, the depth at neighboring contour points
that lie across scanlines might be inconsistent. Thus we in-
validate points whose depth differs from those of the closest
neighboring points along the contour by more than 3mm.
Contour Smoothing.
We further smooth the depth values
along the contour with a 1D mean filter of size
25
pixels. Note
this filter can be implemented very efficiently using a sliding
window technique with two operations per contour pixel.
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Filling.
Finally, we assign occluded and invalidated contour
points to a depth value. This depth value is computed as a
linear combination of the depth assigned to the closest valid
contour points.
Discussion
One important feature of our algorithm is the underlying
computational cost. In general, the complexity of a pixel-
wise stereo matching algorithm can be characterized with
O(W H D)
, where
W
and
H
are the width and height of the
images, respectively, and
D
is the number of tested depth
hypotheses. In many scenarios
D=O(W)
, i.e. higher reso-
lution images imply a larger set of depth hypothesis to be con-
sidered. Coarse-to-fine frameworks for stereo (e.g. [34]) and
randomized stereo correspondence algorithms [
6
] (largely) re-
move the dependency of the runtime complexity on
D
. Since
in our hardware setup the most discriminative regions in the
image are the silhouettes, we restrict the computation of depth
to pixels lying on the silhouette contours. Further, we obtain
a substantial reduction in the search range
D
, since only a
small set of extracted contour pixels in both images can be
potential matches (2-10 points).
1D HAND POSE AND FINGERTIP RECOGNITION
Once we have efficiently estimated the 3D silhouette, this
high-quality contour contains valuable information about the
scene objects, and in particular on the state and pose of any
interacting hands – a key to enable truly interactive scenarios.
There are many options for detecting gestures and salient
features of the hand. One simple approach detects points of
high curvature as fingertip candidates (during contour tracing).
Whilst a heuristic, these types of rules can produce convincing
results [
25
]. However, there are often failure cases, such as
complex poses where knuckles, styluses or other high curva-
ture features confuse the peak detector. To address some of
these issues, we present a novel contour (1D) based hand state
and part classification algorithm, which is complimentary to
more regular peak detection in specific scenarios.
For hand state classification we use a random decision forest
(RDF) [
7
], which has been shown to be effective for both
body [
33
] and hand [
19
] pose estimation using depth images.
These approaches are trained to be invariant to pose and shape
variations, use scale-invariant features, and achieve a robust
result by averaging over multiple pixels. For RetroDepth, we
adapt these approaches to silhouettes, keeping their benefits,
while further reducing the complexity from 2D to 1D.
RDF based contour classification
As a key contribution of this work, our classifiers operate
on 1D contours instead of 2D images. These contours are
essentially 1D sequences of 3D points in world space, sampled
with a rate determined by the camera resolution and setup. For
notational convenience, and without loss of generality, we will
assume that the contours can be parameterized by a variable
s
,
such that
X(s)
gives the world coordinates of the point
s
on
the curve, and a unit step in
s
on the 1D sequence corresponds
to a unit step along the contour in 3D world space.
Each point on the contour is associated with two class labels
ys
and
yf
, where
ys
is a hand shape label (such as pointing,
pinching, grasping or open hand) and
yf
is a fingertip label
(such as index, thumb, or non-fingertip). The task of the
Figure 6: Left: For a given point son the contour, we move
by the offsets u1and u2to reach points X(s+u1)and
X(s+u2). Middle: Hand shape class label ys(one shared
label for all pixels). Right: Fingertip class labels yf.
RDF is to classify the hand shape for the entire contour, and
localize and identify the fingertips.
Decision trees use the internal split nodes to test and guide
the input to one of the leaves, where class distributions of the
output labels are stored. Test functions at split nodes are of
the form:
f(F)< T
, where the function
f
maps the features
F
onto a line, and
T
acts as a threshold. We use the following
test function:
f(s, u1, u2, ~p)=[X(s+u1)X(s+u2)]~p (4)
where
[.]~p
is a projection onto the vector
~p
, and
~p
is one of
the primary axes
~x
,
~y
or
~z
. This test probes two offsets on
the contour, gets their world distance in the direction
~p
, and
this distance is compared against the threshold
T
(Fig. 6).
Because the parameterization of
s
is normalized, the offsets
u1
and
u2
are scale invariant. The test function splits the data
into two sets and sends each to a child node. The quality of a
split is determined by the information gain defined as follows:
G(u1, u2, T ) = H(C)X
s∈{L,R}
|Cs|
|C|H(Cs)(5)
where
H(C)
is the Shannon entropy of the class label distri-
bution of the labels
y
in the sample set
C
, and
CL
and
CR
are the two sets of examples formed by the split. At training
time, multiple features and thresholds are uniformly sampled
from a large range for each split node, and the one with the
highest information gain is selected. Once leaves are reached,
the class distribution of the remaining pixels in the node are
saved. As each pixel has two labels from distinct sets in our
case, we keep two histograms at each leaf: one for hand shape
and one for fingertips.
At run-time, every pixel is evaluated independently with each
tree in the forest and they descend into one of the leaves. For
hand shape classification, the associated distributions read
from the leaves are pooled across the contour to form a final
set of state probabilities. The mode of this distribution is
selected as the hand shape for the contour
X
. The fingertip
localization algorithm is instead more akin to the part clas-
sification method of [
33
]. After classification, we apply a
1D running mode filter to the labeled contour to filter out the
noisy labels. We finally apply connected components to give
labels to the fingers. The system selects the point with the
largest curvature as the fingertip.
To train a single forest that jointly handles shape classification
and fingertip localization, we first disregard the
yf
labels and
calculate the information gain only using
ys
until we reach a
certain depth
m
. From then on, we switch to using
yf
labels
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instead, and do not use
ys
to select features. This has the effect
conditioning each subtree that starts at depth
m
to the shape
class distributions at their roots. This is similar to the idea
presented in [
19
], in that the low level features are conditioned
on the high level feature distribution. However, instead of
using a multi-layered approach, we handle the classification
jointly in a single forest.
We use a realistic synthetic hand model to automatically gen-
erate high-quality hand contours with labels. The hand model
has 32 DoF, which can be used to automatically pose the
object in a range of parameters, with added random noise
on every joint angle. We generate a total of around 8000
left-hand images for each hand state class. These are then
mirrored and given right hand labels. Fingertips are labeled
by mapping the model with a texture that signifies different
regions with separate colors. An example of the hand shape
and fingertip labels for a certain pose can be seen in Fig. 6.
Classifying other object contours
Our method for hand posture classification can also be lever-
aged to classify retro-reflective widgets. Even though the
shapes are mostly primitive shapes e.g. rectangles and circles,
applying template matching is not optimal. This is mainly
because the contour of the object changes while the user is
interacting with it, and also because for every type of object,
a new set of heuristics or rules need to be generated. An
off-the-shelf RDF on the other hand can learn all the variation
of the contours for every shape. It is accurate and fast, and
can learn to recognize new objects at run-time.
To reduce the variation due to occlusion, we first estimate the
convex hull of the objects and evaluate this simplified contour.
We use real data for training, since labeling the contours is
straightforward in this case. The same features and objective
function are used as in Equation 4 and 5. As the contours
are much simpler in this case, much shallower forests are
sufficient for online training.
Recognizing a stylus
Pens are typically much sharper than fingers and therefore
easier to recognize with heuristics. On both small objects
where we have partial hand contours, as well as on larger
surfaces where we have the full hand contour, finding the
sharpest point on the contour (and thresholding it) is a good
estimator for the pen tip location. However, the pen changes
the contour of the hand shape considerably, which reduces the
accuracy of the hand state classifier and causes the fingertip
localizer to fail. Therefore, when a pen is detected on a
large surface, the pen contour points are first removed from
the contour. This can be done by following the pen contour
in both directions until the gradient of the contour changes
abruptly. The corresponding points are then connected to
form the hand contour.
Discussion and results
When users interact with smaller widgets, where only a few
fingers or the pen is visible, hand state recognition is not
desired as we have only a partial hand contour. In these cases,
the pen and fingers can be found by simple peak detection.
When the hand is on the large rectangular surface however,
simple heuristics fail to identify or localize fingers robustly
(e.g. finding peaks at knuckles). Examples that would very
likely be failure cases for standard heuristic-based approaches
Class Thumb Index Middle Ring Pinky Reject
Accuracy0.848 0.754 0.615 0.594 0.796 0.882
Table 1: Per-pixel classification accuracy for the individual
fingertip classes, before filtering.
Figure 7: Examples of results based on complex poses and
visually ambiguous poses. Ground truth data shown left
and test results shown right. Poses shown from left to right:
two pinch gestures, a splayed hand with an occluded finger,
and fused fingers on a splayed hand.
such as detecting pinch [
42
] or fingertips [
25
] are shown in
Fig. 7. Whilst these reinforce the need for a machine learning
approach, our method is also complimentary to more heuristic-
driven approaches.
We conducted experiments with eight hand states, correspond-
ing to left and right handed versions of four hand poses,
namely “pointing”, “pinching”, “open hand looking down”,
and “open hand looking up”. For each hand state we have
4000 synthetic left hand images, which are mirrored to re-
trieve data for the right hand. We train on half of the samples
and test on the remaining ones.
We train an RDF with a single tree until depth 18 with the
state labels only, and calculate the confusion matrix over all
hand states. The RDF reaches perfect accuracy for both left
and right handed versions of three of the command modes,
and only shows confusion between the left– and right–handed
pointing gestures. The success rate of left–handed pointing is
93% and right–handed pointing is 92%, and the corresponding
left-right confusion is 7% for the former and 6% for the latter
case. This gives us an overall command mode classification
accuracy of 100% and left-right hand classification accuracy
of 98% when all states are considered.
The synthetic images also have labels for the five fingertips
and the reject class, corresponding to the non-fingertip areas.
After the state classification training concludes at depth 18,
we continue training with the fingertip labels until depth 25.
The leaves are assigned the distributions of both state and
fingertip labels. The success rates are given in Table 1. Even
though we are operating on 1D contours, with much lower
computation, the per-pixel classification rates are similar to
the reported results in [33] and [19].
CONTOUR INPAINTING
So far we have purposefully pushed the limits of what can
be achieved purely based on silhouettes. However, there are
certain scenarios and legacy applications that require fully
dense depth maps. In this section, we show a robust and fast
method for inpainting the contour of objects. The results of
our contour inpainting algorithm can be found in Fig. 8.
In order to fill the interior
of our segmented contour
,
we formulate the interpolation problem as a Laplace equa-
tion with Dirichlet boundary condition. Hence, contour
depth values define a known scalar-valued boundary func-
tion
ˆ
f(x, y)|
and we need to solve for the interior depth
values f(x, y)over with:
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f(x, y) = 0 (x, y)δ
f(x, y)|=ˆ
f(x, y)|otherwise
Then, we use a parallel Jaccobi/Gauss-Seidel solver on the
GPU to solve the Laplace equation. In order to speed up
convergence, we initialize the interior using a hierarchical pull-
push interpolation with bi-cubic interpolation weights [
27
].
Note that we mark pixel values of
with a negative sign,
which allows us (except for the pull-push hierarchy) to solve
the system in place. The contour inpainting results can be
seen in the supplemental video.
EXPERIMENTS
We now evaluate the performance of our system compared to
three baseline measures. As a first baseline, we choose Leap
Motion (
Leap
) given the widely publicized precision of the
system, and its availability as a commodity sensor. The other
baselines are based on another, arguably more ubiquitous
depth sensor: the Kinect. With this sensor, we chose to run
two conditions. The first (
KinectLight
) uses the RetroDepth
hand posture and fingertip tracking pipeline described previ-
ously, and uses contour-traced Kinect depth maps as input
(instead of RetroDepth stereo data). This in part evaluates
our software pipeline with a commodity sensor, but also gives
us an indication of the precision of the stereo matching al-
gorithm and hardware setup that the full RetroDepth system
supports. The second Kinect baseline (
3Gears
) evaluates the
hand tracker from 3Gear systems [
1
]. This can be considered
the current widely-available state-of-the-art hand tracker for
Kinect with scenarios close to RetroDepth.
3D Target Acquisition
In our first task, we wanted to evaluate one of the core features
of RetroDepth, its 3D input precision. For all conditions,
the same physical setup was used as shown in Fig. 9. This
provided a physical input space of
30x30x30cm
. Leap was
placed on the table, Kinect and RetroDepth mounted top down.
All cameras were configured to share the same center of origin.
A large table was used with retro-reflective material visible
for the RetroDepth condition. This material was removed for
the other conditions to avoid IR interference. A PC with a
6-core 2.8GHz processor and Nvidia GTX480 GPU, and large
display were used for all conditions. Our pipeline ran on this
hardware at 60Hz.
Participants
Twelve participants (9 male, 3 female) be-
tween the ages of 21 and 39 were recruited for the study.
Participants were daily computer users, 2 were left handed.
All had normal vision. Participants had no experience of using
3D hand gesture-systems, but some had experiences playing
Kinect games.
Task
We conducted a 3D targeting task where the physical
interaction volume was subdivided into 27 equally sized cells
Figure 8: Contour inpainting results in false color.
Figure 9: Study setup. Left: Kinect, Leap and RetroDepth
setups for 3D acquisition task. Middle: Kinect touch evalu-
ation. Right: Leap touch evaluation.
Figure 10: Left: touch targeting results, and right: 3D ac-
quisition targeting results.
(3x3x3 grid). The user began by holding their finger in a 3D
transparent box appearing centered and at the front of the
3D scene. After a fixed time, a billboarded target shown in
Fig. 9 (left screen) appeared centered in one cell for users to
select. 3 trials were performed per condition. Starting and
target positions were coupled a priori to reduce in-condition
variance. This set was used across all conditions, with the
order randomized, ensuring that the total distance traveled
was exactly equal across all users, and each condition. Mea-
surement of task completion was triggered once the fingertip
left the starting position and ended once the user entered the
target triggering area. The targets were 5x5cm high.
Procedure
The experiment employed a within-subject re-
peated measure design. The independent variable was the
input type: RetroDepth, Leap, KinectLight, 3Gears. The de-
pendent measure was the targeting accuracy (measured as a
distance of the fingertip to the target center upon selection).
The presentation order of the conditions was counterbalanced
using a Latin Square design across users. A short training
phase was first performed, after which each user was asked
to perform the task as accurately as possible. The entire ex-
periment lasted about 60 minutes. Participants filled out a
post-experiment questionnaire upon completion. Users were
observed at all times as the task was performed, and notes on
subjective experiences were taken.
Results
Our 12 participants produced a total of 3888 selections in this
task. No effect was found between the four blocks of trails
(e.g. from fatigue or learning effects). Additionally there
was no significant differences between participants. This
allows us to combine data to cluster per condition results as
shown in Fig. 10 (right). The plot shows the average of the
target hits across all participants, clustered by condition. Note
we ignore the Z contribution as the targets were planar, and
depth is measured along the Y axis in our 3D scene. The
more compact the cluster the more accurate the result. 95%
confidence ellipses are shown for each condition.
We compute the minimum target sizes required to achieve
95% accuracy for each condition. RetroDepth achieves the
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Figure 11: 2D touch results across all four conditions.
smallest minimum target size of 1.95x1.55cm with error
X=7.89mm and Y=10.90mm. 3Gears achieves the largest
minimum target size of 5.30x3.15cm with error X = 20.86mm
and Y=29.74mm. KinectLight achieves a size of 2.89x2.08cm
with error X= 15.24mm and Y=24.87mm. Leap performs sim-
ilarly achieving a size of 3.18x2.00cm with error X=17.53mm
and Y=22.67mm.
Physical Touch Performance
Another important feature of RetroDepth is the ability to
support touch sensing. To evaluate the performance of the
system against our baseline, we ran a further experiment,
where each camera (Kinect, Leap and RetroDepth) was setup
in top down arrangement Fig. 9 (middle and right). For Leap
this meant adding a sheet of IR absorbing film to the table. A
20cm
×
25cm acrylic sheet was used as a touch surface, with
small laser etched targets (spaced 5cm apart), and sized for
the user’s fingertips. 12 participants, 3 women and 9 men,
were asked to perform a physical touch task. The aim was to
measure system performance in terms of touch accuracy, and
overall sensor precision. Participants were asked to touch all
12 targets in turn over 5 trials. The 3D point was recorded once
the user’s self reported. Fig. 11 shows the X-Y 2D accuracy
plots for each of the sensors. Fig. 10 (left) shows the Z-Y
accuracy plots (Z axis corresponds to depth precision). Based
on these statistics it is possible to define the minimum 3D
target bounds to capture 95% of the touches. For RetroDepth
this is 1.69x0.98x0.75cm, KinectLight is 1.49x1.29x0.85cm,
Leap is 3.88x2.91x 1.63cm, 3Gear is 4.41x3.71x2.78cm.
Discussion
We found the results extremely encouraging given the preci-
sion of RetroDepth compared to these baselines. It is however
important to note that we are not comparing like-for-like nec-
essary. In terms of fidelity, for example, Leap uses lower cost
cameras, and we use more costly research cameras. Nor was
the Leap designed for touch-like scenarios (i.e. pointing down-
wards). Although we feel that the touch accuracy of the Leap
is good enough to warrant future work on this scenario, based
on our IR absorbing idea. On the other hand, RetroDepth is
a research prototype whilst others are engineered products.
Our system also offers different data (silhouettes) than the
Leap and the Kinect, and none of these elements are being
measured. However, with all this in mind our results are
encouraging in terms of precision and performance.
An interesting finding is how well Kinect performed in the
KinectLight condition, particularly for touch sensing. This
illustrates benefits of our software pipeline for improving
touch and 3D acquisition. Another interesting finding is that
user’s perceptions and performance can be very different.
Leap surprisingly did not perform as well as expected, yet
it was ranked a close second to RetroDepth in terms of pref-
erence. Many users described it as fluid, which indicates
that the higher frame-rate has a strong impact in terms of
NUI experience, potentially even distorting views of sensor
accuracy. The 3Gear performance also gives an interesting
insight. Whilst the system appeared to be performing well,
showing compelling 3D models fitting the Kinect data, this
model fitting approach ultimately led to severe inaccuracies
for touch and selection, as there will always be discrepancies
between a generic model and the user’s hand.
Overall we feel that RetroDepth offers an exciting new re-
search platform for future work on NUI interfaces, leading
to new applications that incorporate touch, pressure, 3D, and
even tangible input. There are of course limitations with our
approach. Ultimately, 3D silhouettes are a lower dimensional
representation of the real world, and in that regard data can be
lost. The interesting finding from our experiments is just how
much information is encoded in the silhouette allowing high
accuracy rates for hand pose and part classification. Another
clear finding is around frame rate, where user feedback sug-
gests that trading some precision for speed may be desirable,
and where there are significant computational advantages of
working with silhouettes.
CONCLUSIONS
In this paper we have presented RetroDepth a system that
allows for high-precision 3D sensing on and above physical
surfaces. We have provided several key contributions: 1)
A fully working real-time 3D silhouette sensing technology
which combines (for the first time) the common NUI modali-
ties of touch, pressure, stylus, objects, and 3D, into a single
system. 2) a new stereo matching algorithm for computing
fast and precise 3D contours. 3) a new 1D contour classifica-
tion algorithm, for robustly identifying physical tools, hand
poses and salient features. 4) A software pipeline with practi-
cal impact even for regular Kinect sensors.
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Session: On and Above the Surface
CHI 2014, One of a CHInd, Toronto, ON, Canada
1386
... Besides using unmodified physical keyboards, there have been several approaches in extending the basic input capabilities of physical keyboard beyond individual button presses. Specifically, input on, above and around the keyboard surface have been proposed using acoustic [171,189], pressure [86,208,357], proximity [316], capacitive sensors [35,102,142,268,298,323], cameras [175,260,344], body-worn orientation sensors [50] or even unmodified physical keyboards [193,Fig. 18. ...
Preprint
This chapter gives an overview of interaction techniques for mixed reality including augmented and virtual reality (AR/VR). Various modalities for input and output are discussed. Specifically, techniques for tangible and surface-based interaction, gesture-based, pen-based, gaze-based, keyboard and mouse-based, as well as haptic interaction are discussed. Furthermore, the combination of multiple modalities in multisensory and multimodal interaction, as well as interaction using multiple physical or virtual displays, are presented. Finally, interaction with intelligent virtual agents is considered.
... There are various technologies for tracking user-defined elements on a surface, all having their advantages and disadvantages. With cameras, for example, ShadowTracking [9] recognizes silhouette shadows from above a well-lit touch screen, VoodoSketch [3] and RetroDepth [20] apply special retro-reflective surfaces. DIRECT [39] combines RGBD and IR input. ...
Conference Paper
User interfaces rarely adapt to the specific user preferences or the task at hand. We present a method that allows to quickly and inexpensively create personalized interfaces from plain paper. Users can cut out shapes and assign control functions to these paper snippets via a simple configuration interface. After configuration, control takes place entirely through the manipulation of the paper shapes, providing the experience of a tailored tangible user interface. The shapes and assignments can be dynamically changed during use. Our system is based on markerless tracking of the user's fingers and the paper shapes on a surface using an RGBD camera mounted above the interaction space, which is the only hardware sensor required. Our approach and system are backed up by two studies where we determined what shapes and interaction abstractions users prefer, and verified that users can indeed employ our system to build real applications with paper snippet interfaces.
... There have been several approaches in extending the basic input capabilities of physical keyboard beyond individual button presses. Specifically, input on, above and around the keyboard surface have been proposed using acoustic [38,43], pressure [17,52,81], proximity [74], capacitive sensors [5,21,32,64,71,76], cameras [39,62,80], body-worn orientation sensors [9] or even unmodified physical keyboards [46,82]. Besides sensing, actuation of keys has also been explored [4]. ...
Preprint
Physical keyboards are common peripherals for personal computers and are efficient standard text entry devices. Recent research has investigated how physical keyboards can be used in immersive head-mounted display-based Virtual Reality (VR). So far, the physical layout of keyboards has typically been transplanted into VR for replicating typing experiences in a standard desktop environment. In this paper, we explore how to fully leverage the immersiveness of VR to change the input and output characteristics of physical keyboard interaction within a VR environment. This allows individual physical keys to be reconfigured to the same or different actions and visual output to be distributed in various ways across the VR representation of the keyboard. We explore a set of input and output mappings for reconfiguring the virtual presentation of physical keyboards and probe the resulting design space by specifically designing, implementing and evaluating nine VR-relevant applications: emojis, languages and special characters, application shortcuts, virtual text processing macros, a window manager, a photo browser, a whack-a-mole game, secure password entry and a virtual touch bar. We investigate the feasibility of the applications in a user study with 20 participants and find that, among other things, they are usable in VR. We discuss the limitations and possibilities of remapping the input and output characteristics of physical keyboards in VR based on empirical findings and analysis and suggest future research directions in this area.
... A gesture-recognition system based on the wearable devices can accurately reflect the movement intention of the human body, but measurement equipment needs to be attach to the human body, which has a negative impact on the user experience. The application of gesture-recognition systems based on visual information is very promising [3,4], but there are some limitations in some respects. When the lighting conditions are insufficient, the background is complicated, or the hand is in front of the body, it is extremely difficult to completely separate and extract the human hand from the background and the body [2]. ...
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Currently, gesture recognition based on electric-field detection technology has received extensive attention, which is mostly used to recognize the position and the movement of the hand, and rarely used for identification of specific gestures. A non-contact gesture-recognition technology based on the alternating electric-field detection scheme is proposed, which can recognize static gestures in different states and dynamic gestures. The influence of the hand on the detection system is analyzed from the principle of electric-field detection. A simulation model of the system is established to investigate the charge density on the hand surface and the potential change of the sensing electrodes. According to the simulation results, the system structure is improved, and the signal-processing circuit is designed to collect the signal of sensing electrodes. By collecting a large amount of data from different operators, the tree-model recognition algorithm is designed and a gesture-recognition experiment is implemented. The results show that the gesture-recognition correct rate is over 90%. With advantages of high response speed, low cost, small volume, and immunity to the surrounding environment, the system could be assembled on a robot that communicates with operators.
... Most recent applications of device-free human sensing make use of sensors that detect perturbations of visible light, sound, or radio waves due to certain human activities. Visible light-based approaches may employ either cameras [17,39,40] or a combination of light emitting diodes (LEDs) and photo detectors [19,22,23,49]. Camera-based systems apply computer vision techniques on highresolution images to track human motion or detect certain activities, while shadow patterns are processed in systems that use LEDs and photo detectors. ...
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Full-text available
Recent research has demonstrated new capabilities in radio frequency (RF) sensing that apply to health care, smart home, and security applications. However, previous work in RF sensing requires heavy utilization of the radio spectrum, for example, transmitting thousands of WiFi packets per second. In this paper, we present a device-free human sensing system based on received signal strength (RSS) measurements from a low-cost single carrier narrowband radio transceiver. We test and validate its performance in three different applications: real-time heart rate monitoring, gesture recognition, and human speed estimation. The challenges in these applications stem from the very low signal-to-noise ratio and the use of a single-dimensional measurement of the channel. We apply a combination of linear and non-linear filtering, and time-frequency analysis, and develop new estimators to address the challenges in the particular applications. Our experimental results indicate that RF sensing based on single-carrier magnitude measurements performs nearly as well as the state-of-the-art while utilizing three orders of magnitude less bandwidth.
... In [52,53], Tang, et al. further explore variants of regression forest. RetroDepth [27] senses 3D silhoue es of hands using a retro-re ector to separate hands from the background. ...
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Full-text available
Free-hand gestural input is essential for emerging user interactions. We present Aili, a table lamp reconstructing a 3D hand skeleton in real time, requiring neither cameras nor on-body sensing devices. Aili consists of an LED panel in a lampshade and a few low-cost photodiodes embedded in the lamp base. To reconstruct a hand skeleton, Aili combines 2D binary blockage maps from vantage points of different photodiodes, which describe whether a hand blocks light rays from individual LEDs to all photodiodes. Empowering a table lamp with sensing capability, Aili can be seamlessly integrated into the existing environment. Relying on such low-level cues, Aili entails lightweight computation and is inherently privacy-preserving. We build and evaluate an Aili prototype. Results show that Aili’s algorithm reconstructs a hand pose within 7.2 ms on average, with 10.2° mean angular deviation and 2.5-mm mean translation deviation in comparison to Leap Motion. We also conduct user studies to examine the privacy issues of Leap Motion and solicit feedback on Aili’s privacy protection. We conclude by demonstrating various interaction applications Aili enables.
... To overcome these constraints, some researchers proposed touch detection techniques using 3D cameras (e.g. Kinect) [2,3,7]. Most of these systems capture the 3D geometry of a surface beforehand and detect touch by recognizing the distance between the surface and the approaching finger. ...
Conference Paper
In this paper we try to realize a system that enables users to interact with surrounding surfaces using touch interactions. For this purpose, we propose new touch detection technique which utilizes the shadows of a finger, and developed a prototype system with an infrared (IR) camera and two IR lights. Since the shapes of a finger's shadows vary drastically depending on the distance between the surface and finger, our prototype system can detect touch. To improve the accuracy of the estimated touch position, we also introduce multiple regression analysis into the estimation algorithm of the touch position. We conducted two experiments on the accuracy of estimated touch position, and the results shows that the target accuracy was within an error of less than 5 mm.
Article
Physical keyboards are common peripherals for personal computers and are efficient standard text entry devices. Recent research has investigated how physical keyboards can be used in immersive head-mounted display-based Virtual Reality (VR). So far, the physical layout of keyboards has typically been transplanted into VR for replicating typing experiences in a standard desktop environment. In this paper, we explore how to fully leverage the immersiveness of VR to change the input and output characteristics of physical keyboard interaction within a VR environment. This allows individual physical keys to be reconfigured to the same or different actions and visual output to be distributed in various ways across the VR representation of the keyboard. We explore a set of input and output mappings for reconfiguring the virtual presentation of physical keyboards and probe the resulting design space by specifically designing, implementing and evaluating nine VR-relevant applications: emojis, languages and special characters, application shortcuts, virtual text processing macros, a window manager, a photo browser, a whack-a-mole game, secure password entry and a virtual touch bar. We investigate the feasibility of the applications in a user study with 20 participants and find that, among other things, they are usable in VR. We discuss the limitations and possibilities of remapping the input and output characteristics of physical keyboards in VR based on empirical findings and analysis and suggest future research directions in this area.
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StarLight is an infrastructure-based sensing system that reuses light emitted from ceiling LED panels to reconstruct fine-grained user skeleton postures continuously in real time. It leverages only a few (e.g., 20) photodiodes at optimized locations on the floor to capture low-level visual cues (light blockage information), with neither invasive cameras nor on-body sensors. It aggregates the blockage information of different light rays and identifies the best-fit 3D skeleton postures. Experiments show that StarLight achieves 13.6° mean angular error for five body joints and reconstructs a mobile skeleton at a high frame rate (40 FPS). StarLight presents a new unobtrusive sensing paradigm to augment today's mobile sensing for continuous and accurate behavioral monitoring.
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We transform an LCD into a display that supports both 2D multitouch and unencumbered 3D gestures. Our BiDirectional (BiDi) screen, capable of both image capture and display, is inspired by emerging LCDs that use embedded optical sensors to detect multiple points of contact. Our key contribution is to exploit the spatial light modulation capability of LCDs to allow lensless imaging without interfering with display functionality. We switch between a display mode showing traditional graphics and a capture mode in which the backlight is disabled and the LCD displays a pinhole array or an equivalent tiled-broadband code. A large-format image sensor is placed slightly behind the liquid crystal layer. Together, the image sensor and LCD form a mask-based light field camera, capturing an array of images equivalent to that produced by a camera array spanning the display surface. The recovered multi-viewc orthographic imagery is used to passively estimate the depth of scene points. Two motivating applications are described: a hybrid touch plus gesture interaction and a light-gun mode for interacting with external light-emitting widgets. We show a working prototype that simulates the image sensor with a camera and diffuser, allowing interaction up to 50 cm in front of a modified 20.1 inch LCD.
Book
Today the cost of solid-state two-dimensional imagers has dramatically dropped, introducing low cost systems on the market suitable for a variety of applications, including both industrial and consumer products. However, these systems can capture only a two-dimensional projection (2D), or intensity map, of the scene under observation, losing a variable of paramount importance, i.e., the arrival time of the impinging photons. Time-Of-Flight (TOF) Range-Imaging (TOF) is an emerging sensor technology able to deliver, at the same time, depth and intensity maps of the scene under observation. Featuring different sensor resolutions, RIM cameras serve a wide community with a lot of applications like monitoring, architecture, life sciences, robotics, etc. This book will bring together experts from the sensor and metrology side in order to collect the state-of-art researchers in these fields working with RIM cameras. All the aspects in the acquisition and processing chain will be addressed, from recent updates concerning the photo-detectors, to the analysis of the calibration techniques, giving also a perspective onto new applications domains. © 2013 Springer-Verlag Berlin Heidelberg. All rights are reserved.
Conference Paper
Vision based articulated hand pose estimation and hand shape classification are challenging problems. This paper proposes novel algorithms to perform these tasks using depth sensors. In particular, we introduce a novel randomized decision forest (RDF) based hand shape classifier, and use it in a novel multi–layered RDF framework for articulated hand pose estimation. This classifier assigns the input depth pixels to hand shape classes, and directs them to the corresponding hand pose estimators trained specifically for that hand shape. We introduce two novel types of multi–layered RDFs: Global Expert Network (GEN) and Local Expert Network (LEN), which achieve significantly better hand pose estimates than a single–layered skeleton estimator and generalize better to previously unseen hand poses. The novel hand shape classifier is also shown to be accurate and fast. The methods run in real–time on the CPU, and can be ported to the GPU for further increase in speed.
Conference Paper
HoloDesk is an interactive system combining an optical see through display and Kinect camera to create the illusion that users are directly interacting with 3D graphics. A virtual image of a 3D scene is rendered through a half silvered mirror and spatially aligned with the real-world for the viewer. Users easily reach into an interaction volume displaying the virtual image. This allows the user to literally get their hands into the virtual display and to directly interact with an spatially aligned 3D virtual world, without the need for any specialized head-worn hardware or input device. We introduce a new technique for interpreting raw Kinect data to approximate and track rigid (e.g., books, cups) and non-rigid (e.g., hands, paper) physical objects and support a variety of physics-inspired interactions between virtual and real. In particular the algorithm models natural human grasping of virtual objects with more fidelity than previously demonstrated. A qualitative study highlights rich emergent 3D interactions, using hands and real-world objects. The implementation of HoloDesk is described in full, and example application scenarios explored. Finally, HoloDesk is quantitatively evaluated in a 3D target acquisition task, comparing the system with indirect and glasses-based variants.
Conference Paper
Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias by estimating an individual 3D plane at each pixel onto which the support region is projected. The major challenge of this approach is to find a pixel’s optimal 3D plane among all possible planes whose number is infinite. We show that an ideal algorithm to solve this problem is PatchMatch [1] that we extend to find an approximate nearest neighbor according to a plane. In addition to Patch-Match’s spatial propagation scheme, we propose (1) view propagation where planes are propagated among left and right views of the stereo pair and (2) temporal propagation where planes are propagated from preceding and consecutive frames of a video when doing temporal stereo. Adaptive support weights are used in matching cost aggregation to improve results at disparity borders. We also show that our slanted support windows can be used to compute a cost volume for global stereo methods, which allows for explicit treatment of occlusions and can handle large untextured regions. In the results we demonstrate that our method reconstructs highly slanted surfaces and achieves impressive disparity details with sub-pixel precision. In the Middlebury table, our method is currently top-performer among local methods and takes rank 2 among approximately 110 competitors if sub-pixel precision is considered. 1
Article
Prototyping gestural and multitouch applications for desktop and mid-air interaction still requires expensive non-portable equipment, complex setup and calibration, and often reimplementation of tracking algorithms. We present Interactive Space, a prototyping framework designed to ease exploration of touch and gestural interaction in real-world settings. For us this is a first step in developing interfaces for electronic medical records to be evaluated in clinical settings. The main contribution is a simple flexible system that supports interaction on and above the desktop, and includes a semi-automatic calibration mechanism that makes it highly portable. We describe the framework, SDK, calibration method, limitations, availability, and a preliminary evaluation.
Article
Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
Article
Digital tabletop environments offer a huge potential to realize application scenarios where multiple users interact simultaneously or aim to solve collaborative tasks. So far, research in this field focuses on touch and tangible interaction, which only takes place on the tabletop’s surface. First approaches aim at involving the space above the surface, e.g., by employing freehand gestures. However, these are either limited to specific scenarios or employ obtrusive tracking solutions. In this paper, we propose an approach to unobtrusively segment and detect interaction above a digital surface using a depth sensing camera. To achieve this, we adapt a previously presented approach that segments arms in depth data from a front-view to a top-view setup facilitating the detection of hand positions. Moreover, we propose a novel algorithm to merge segments and give a comparison to the original segmentation algorithm. Since the algorithm involves a large number of parameters, estimating the optimal configuration is necessary. To accomplish this, we describe a low effort approach to estimate the parameter configuration based on simulated annealing. An evaluation of our system to detect hands shows that a repositioning precision of approximately 1 cm is achieved. This accuracy is sufficient to reliably realize interaction metaphors above a surface.