Computer Vision Based 3D Reconstruction : A Review

Abstract

3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.

Full text

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 4, August 2019, pp. 2394∼2402
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i4.pp2394-2402 r2394
Computer vision based 3D reconstruction : A review
Hanry Ham, Julian Wesley, Hendra
Computer Science Department, School of Computer Science, Bina Nusantara University, Indonesia
Article Info
Article history:
Received Jan 15, 2018
Revised Jan 23, 2019
Accepted Mar 4, 2019
Keywords:
3D alignment
3D point clouds
3D reconstruction
ABSTRACT
3D reconstruction are used in many fields starts from the object reconstruction such
as site, cultural artifacts in both ground and under the sea levels, medical imaging
data, nuclear substantional. The scientist are beneficial for these task in order to learn,
keep and better visual enhancement into 3D data. In this paper we differentiate the
algorithm used depends on the input image: single still image, RGB-Depth image,
multiperspective of 2D images, and video sequences. The prior works also explained
how the 3D reconstruction perform in many fields and using various algorithms.
Copyright c
2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Hanry Ham,
Computer Science Department,
School of Computer Science, Bina Nusantara University,
Jakarta, 11480 - Indonesia.
Email: hanry.ham@binus.edu
1. INTRODUCTION
3D Reconstruction task is one of the interesting task that meet its maturity already. These can be
seen from the commercial products such as product from Agisoft and Pix4D that are capable of produced high
quality of large scale 3D models. Furthermore, the hardware such as the computer vision has been developed
and improve since then. There are some setup camera introduced in the research such as stereo camera and
Kinect.
In addition to the vision setup, kinect camera shows a great positive feedback from the researchers,
proved by common vision setup that can be found in the literature review. Not only that, stereo camera setup
can be found among the literature review. In addition to the stereo camera, custom stereo camera are quite
popular among the researchers by combining two equals web camera that positioned by period of distance.
The algorithm to perform 3D reconstruction between these camera are different due to the produced images are
different as well. Kinect abilities allows RGB image and depth map produced, on the other hand Stereo camera
has to perform another depth map acquisition algorithm by combining 2 RGB images.
Numerous numbers of 3D reconstruction task can be found in capturing the site, cultural artifacts
both in ground and under the sea levels [1]. The extinction factor is the most prominent issue in these area.
Moreover, 3D imaging data also could help improve the accuracy of the anatomical features in order to observe
some areas before coming to the surgery action .Furthermore, in order to perform 3D reconstruction, there are
multiple approaches found in the literature review such as from the broad ranges of vision setup, various types
of inputted image to construct 3D reconstruction. Thus, In this paper will describe more on those approaches.
The great numbers of the researchers along with the hardware supports allows such algorithm to do
high processing calculation in order to perform reconstruction task. There are some sections mentioned in part
2.. The benefits of reconstruction are to perform 3D recording, visualization, representation and reconstruction
[2]. Moreover Tsiafaki and Michailidou explained that, there are 6 benefits in performing reconstruction and
visualization: limiting the destructive nature of excavating, placing excavation data into the bigger picture,
limiting fragmentation of archaeological remains, classifying archaeological finds, limiting subjectivity and
publication delays, enriching and extending archaeological research.
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Some algorithms found in the literature review introduced the usage of single and multiple images
approaches to perform 3D reconstruction. There are some characteristics of the algorithms in the literature
specifically built for single or multiple images, advantages and drawbacks explained in this paper.
In this paper will described the vision setup by 3 categories as follows:
1. Single Camera
A single camera is simple to calibrate, computationally efficient more compact. However, they are lack
of the depth information. It requires prior knowledge from other sensor to determine the depth scale [3].
2. Stereo Camera
In stereo camera mechanism is that the images captured either using 2 equals web camera [4] or any
cameras. They are set by a defined distance. In addition to 2 images captured, an algorithm is used
to generate depth map. However, stereo matching have several issue when the scene contains weekly
textured areas, repetitive patterns or occlusions occur in both indoor and outdoor environments [5] as
shown in Figure 1.
Figure 1. Stereo Camera
3. Kinect / Structured Light / Time of Flight
Structured Light sensor is able to perform range detection, an accurate distance measurement is the
output [6]. Kinect camera is a product from Microsoft that has an RGBD camera. The product comes
with native SDK that allows user to call the API to perform some vision task such as skeleton detection.
4. Fusion
Some researchers also tried possibilities of using fusion approach where as combining depth map pro-
duced by Stereo and kinect camera to achieve higher accuracy in depth map precision. To such develop-
ment allows to produce better 3D Reconstruction object, rich in features details. Range cameras are low
cost and ease to use to construct 3D point clouds in real time. One issue arise is that the transparent and
reflective surfaces [7]. on the other hand, 3D model produced by stereo vision are mostly incomplete
in low texture regions. The possibilities of combining both approached could lead to better depth map
quality. Fusion approach is shown in Figure 2.
Figure 2. Fusion Approach
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The algorithms vary due to the characteristics of the inputted image. Therefore, in this paper we
described the inputted image into 2 categories : single and multiple images. Single image, the characteristic
image can be described as:
1. Single Still Image
Single still image here using an RGB image. This image can be taken by a regular camera.
2. RGB-Depth Image
RGB image is taken with the setup camera that produced RGB-D format image. Mostly, the setup used
is commercial camera such as Kinect, Intel real sense camera.
On the other hand, the multiple images can be described as:
1. Multiperspective of 2D images [8]
The idea of this aprroach is to take some images differentiate in its perspective to the object. Thus the
area of the object are covered properly using filter [9]. In addition to that, Xian-hua and Yuan-qing
[10] said that in order to perform 3D reconstruction, an effective matching of a feature is the prominent
factor in later stage. They implemented a feature matching error elimination method based on collision
detection.
2. Video Sequences
The using of the input video sequences as known as structure from motion. Sepehrinour and Kasaei
explained that these methods are using the shared information of consecutive frames, in the form of
tracking information of feature points in a sequence of images. The factors may impact to the developed
methods: the knowledge or lack of knowledge of camera calibration parameters, having multiple cameras
with different viewing angles or only one moving camera, and rigid or non-rigid shape reconstruction
based on the incoming video stream.
2. TAXONOMY OF 3D RECONSTRUCTION
3D Reconstruction plays an important roles in several aspects such as medical imaging data, site and
cultural artifact reconstruction.
(a) Medical Imaging Data
Common surgery operation procedures uses X-Ray as a reference for the doctor to operate on specific
section. However, some important features cannot be visualized well in 2D images [12]. In addition
to 2D images, the accuracy may increase depends on several aspects such as: number of 2D Views, the
image noise, and the image distortion. Magnetic resonance Images also holds an important method while
considering the operation process. The given output of MRI are in 2D images, however there are some
literature can be found in manipulating those images into 3D space. By implementing such method, they
would like to prove the more features captures, the more accurate result is. a work from Hichem et al.
introduced a geometric interpretation of the 3D model reconstruction of the blood vessel of the human
retina. Sumijan et al. [14] in their work introduced a method to calculate volume Hemorrhage Brain
on CT-Scan Image and 3D Reconstruction. The idea of this work is to calculate of the bleeding area in
the brain on each image slide CT-scan. As it is said in the previous work[15], brain injury is one of the
most causes that cause the death of human. In addition to the pipeline, the extraction the bleeding area
of the brain using Otsu algorithm combining with the morphological features algorithm. Therefore by
visualizing the brain volume aim at improving visual enhancement for the doctor to give the best medical
treatment.
(b) Site and cultural artifacts Reconstruction
The site reconstruction has been widely an issue to the archaeology in order to capture the social, culture
through the building, they do the reconstruction. Regular camera can only allow to capture in 2D space
format. Not all the details from the building can be captured and closely observed. Since then, by
using stereo camera or Kinect make this task possible along with the algorithm developed in the current
research. The archaeological sites are not only on the ground but also under the sea. The reconstruction
which performed under the sea rises another issue to the images captured such as degradation quality
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if underwater images, uneven illumination of light on the surface of objects. scattering and absorption
effects [1].
(c) Nuclear Substantial Reconstruction
Monterial et al. [16] used 3D image reconstruction of neutron sources that emit correlated gammas. This
aim at preventing nuclear threat search, safeguards and non-proliferation. This research is prominent and
under supervision of legal division. In addition to that, nuclear had been used as source of energy, yet
some controversies arise about the impact of harmful substantial.
2.1. Single still image approach
The first part will describe the algorithms found in the literature review using single still image. Com-
pared to the multiple images, single image occurs tend to have more challenges. Saxena et al. explained that
one of the issued is to create a depth map due to the local features are insufficient to estimate depth at a point.
In addition, single still image approach is relatively less studied in the literature.
Saxena et al. [17] introduced a 3D depth reconstruction using a single still image. A supervised
learning approach was proceeded by taking a training set including the unstructured indoor and outdoor en-
vironments and their corresponding ground-truth depthmaps. Their proposed algorithms aware of the global
structure of the image, based on modeling depths and relationships between depths using proposed multiple
spatial scales using a hierarchical, multiscale Markov Random Field. Ground truth were taken using 3D scan-
ner.
Yan et al. [8] proposed a system called Perspective Transformer nets. The model was built by ignoring
the color and texture factors. In addition to that, the experiments shows that excellent performance of the pro-
posed model in reconstructing the object without ground-truth 3D volume as supervision. The input used were
provided by Chang et al. [18] works. The images input proposed is a single view 3D volume reconstruction
[19] with perspective transformation [20] run through defined encoder-decoder network that consists of a 2D
convolutional encoder, a 3D up-convolutional decoder and a perspective transformer networks.
Fan et al. [21] applied a region-based growing algorithm for 3D reconstruction by using brain MRI
images. There are 3 steps in their proposed pipeline : First, the seed element is the initial state of the segmenta-
tion. Second, start the growing process from the seed element. There are 4 areas of the growth area. However
there are some defined threshold value to meet the pattern of growth. Third, use the points which satisfy the
growing requirement as seed element, and continue to grow. in addition to the result, their proposed method
could achieve 90.52% compared to Nadu [22] works.
2.2. RGB-depth image approach
Zhang et al. [23] developed a feature-based RGBD camera pose optimization for real-time 3D recon-
struction. Their proposed work are ignoring corner-based feature detectors such as BRIEF and FAST due to
acquired images contains huge noise around object contours. Subsequently, SURF detector was chosen due to
the fact that its robustness, stability, scaleable and rotation invariant [24]. In addition to that, SURF can be com-
puted in parallel on the GPU [25]. The miss-matched pairs in feature matching can be removed using RANSAC
algorithm. The consistency of the global positions of matched features are tracked by proposed feature cor-
respondence list and camera pose optimization both in the spatial and temporal dimension. Subsequently, in
order to evaluate the method, voxel-hashing was used for each camera poses compared to the proposed method.
It is proved that their proposed optimized camera poses outperforms the structure of the reconstruct model for
the real scene data captured by a fast moving camera.
Group et al. [26] explained that a fully convolutional 3D denoising autoencoder neural network. They
experimented using RGBD dataset and it is proved that the network could reconstruct a full scene from a single
depth image by filling holes and hidden element. The network is capable of learn the object shape by inferring
similarities in geometry. A real-word dataset of table top scenes [27] was used using KinectFusion. Their steps
can be mentioned as follows: acquisition RGBD image using Kinect, denoising and hole filling depth channel
using [28] algorithm, projection of the pixel into 3D space using preset equations, retrieve sensor pose from
accelerometer and align point cloud data, voxelize the point cloud, and A predefined CNN layer was trained. In
addition to that, the network is not constrained to a fixed 3D shape and it is capable successfully reconstructing
arbitrary scenes.
Jaiswal et al. [29] used Kinect to assess 3D object modelling. The proposed pipeline are as follows:
first, 3D point cloud, a green surface was placed behind and under the object to do the histogram-based seg-
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mentation out the object from the RGB images. Afterwards, RANSAC algorithm is used to perform a coarse
alignment. Second, the registration using SIFT based [30] to overcome the lack structural features or undergo
significant changes in camera view. Third, global alignment is used ti eliminate inaccuracy at each registration
that could lead to significant misalignment between the first and last frame. Fourth, 3D point cloud denoising is
performed to refine the 3D object model, in this case Moving Least Square (MLS) 3D model denoising method
[31]. Fifth, surface reconstruction using Delaunay triangulation method [32] to convert 3D point clouds into
meshed. Afterwards, coloring task is performed to each vertex and simply interpolate the color in each triangle
faces.
2.3. Multiperspective of 2D images approach
Kowalski et al. [33] created an open source system for live, 3D data acquisition using multiple kinect
v2 Sensors. To overcome the ability of the native Kinect V2 SDK, they made this flexible framework. There
are 3 coordinates system of a markers: Kinect v2 sensor, coordinate system of a marker which is located at a
center on a given marker and the world coordinate. The proposed pipeline as follows: first calibrations were
done by calibrating 2 types of defined markers. Subsequently the Iterative Closest Points (ICP) algorithm [34]
was used to refine the initial estimation.
Evangelidis et al. [5] combined low-resolution depth data with high resolution stereo data to overcome
the construction of high-resolution depth maps for the range-stereo fusion problem. The input used stereo
images (high resolution) and depth data (low resolution) from the range camera. The low resolution depth data
are projected into the color data and refined a high resolution sparse disparity map. Subsequently, the depth
up-sampling algorithms were perform such as triangulation-based interpolation and join bilateral filter. then a
region growing fusion were performed and final denser High resolution map as the result.
Burns [35] introduced a texture super resolution (TSR) method for 3D multi-view reconstruction. In
addition, their work used video sequence as the input. Moreover to the proposed pipeline, a Photoscan from
Agisoft is used to do multi-view stereo reconstruction and 3D mesh model. Then, optical flow algorithm is
integrated in order to register each pixel of neighboring to the closest key-frame using KLT feature tracker
[36]. Afterwards, to support robustness to outliers the fundamental matrix filtering of the tracked 2D points
and RANSAC filtering of the 2D/3D correspondences. Due to the piece-wise affine surface approximation
constructed in 3D mesh, this may lead to pixels registration error. To overcome that issue, to locate the dis-
placements, an optical flow estimation is used [37]. The object used is 2mx1m desk that has many textured
objects on it as gray-scale images along with the subsampling applied to it. It is acquired using a camera with
5.5mm focal length at f/2.8 mounted on a Byaer 1/18” e2v detector. There are 3 experiments conducted and it
shows that the proposed methods outperforms compared to the registration with mesh and camera poses only,
registration with optical flow only.
Tulsiani et al. [38] studied multi-view supervision for single-view reconstruction and a differentiable
ray consistency (DRC) term was introduced which allows computing gradients of the 3D shape given an ob-
servation from an arbitraty view. The dataset used is called ShapeNet dataset. The following steps to perform
their methods are: formulation, view consistency loss function is introduced aim at measuring the inconsistency
between a predicted 3D share and a corresponding observation image.shape representation, The assumption
made was it is possible to trace trays accross the voxel grid and compute intersection with cell boundaries. The
3D shape representation is parametrized in a discretized 3D voxel grid. Observation, This aim at achieving
the shape to be consistent with some available observation such as depth image, object foreground mask. Also
CNN model was used as a simple encoder-decoder which predicts occupancies in a voxel grid from the input
RGB image. The result outperformed all the algorithms found in the literature review.
Martin-Brualla et al. [39] extended 3D time-lapse reconstruction where a virtual camera moves con-
tinuously in time and space using internet photos. Previous work assumed a static camera, the addition of
camera motion during the time-lapse produces a very compelling impression of parallax. The first step is a
pre-processing step, computing 3D pose of the inputted image using structure from motion algorithm. Sub-
sequently, the desired path has to be specified through the reconstructed scene. Then, the algorithm compute
time-varying, temporally consistent depthmaps for all output frames in the sequences. Proposed 3D time-lapse
reconstruction computes time varying, regularized color profiles for 3D tracks in the scene. output video frames
are reconstructed from the projected color profiles.
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2.4. Video sequences
Sepehrinour and Kasaei [11] introduced a novel algorithm for perspective projection reconstruction
using single view videos of non-rigid surfaces. The system input is a single view video that taken in a totally
natural environment. In addition to that, the features extracted: projective depth coefficients of all points in
each of the input frames, projection matrix components (camera calibration, rotation matrix, and transmission
vector).
Xu et al. [40] developed underwater 3D object reconstruction with multiple views in video stream
via structure from motion (SFM). They are trying to capture the inherent geometrical variation of 3D objects
at multiple visual angles using a myring streamline AUV system with CCD camera with resolution of 480
TVL/PH and the minimum scene illumination 0.28 lux on board. The proposed pipeline : continuous videos
stream combining SFM with object tracking strategies. An object tracking so called particle filter has been
introduced in image sequence with multiple views to focus on the motion trajectories of underwater 3D objects
all the time. a process of triangulation, iterative process, and other parameter adjustment is set for SFM algo-
rithm to recover and estimate the position of the camera calibration and the geometry of underwater scene with
sparse 3D point cloud.
Lapandic et al. [41] introduced a framework for automated reconstruction of 3D model from multiple
2D Aerial images using Unmanned Aerial Vehicle (UAV). The objective of this work is to achieve near real-time
performance with reliable accuracy and execution time. The proposed pipeline as follows: feature detection
and extraction using FAST algorithm and Lucas-Kanade method respectively, 2D point correspondence, point
cloud filtering, camera pose estimation, points triangulation and point cloud calculation.
3. DISCUSSION
The oldest paper cited in this paper is 1981 and the research about 3D reconstruction is still going
on. This proved that the maturity of the research in this area is achieved. There are numerous algorithms is
described in solving numerous of problems. In addition, the commercial software such as Microsoft, Agisoft,
intel real sense, asus and many others companies develop software and hardware to perform such calculation.
The general pipelines found in the literature reviews are: first, image acquisition. There are some datasets
available that can be used in order to evaluate the performance of the proposed algorithms. Moreover, chances
to create own object using vision setup mentioned earlier in section ??. Second, Pre-processing step by allowing
some filters applied to get the best images to construct. Third, 3D cloud points. The alignment algorithm
plays an important role to get decent accuracy. Along with the refinement method in mismatched 3D cloud
registration. Fourth, 3D reconstruction is where the texturing and meshed are applied as the final result.
4. CONCLUSION
In this paper explains several current 3D reconstruction methods from literature review. There are
various algorithm in order to perform each step of general algorithm of 3D reconstruction. Each object con-
structed required special algorithms depends on the vision setup, the texture and size of the observed object.
The improvement of the sensor could lead to the higher accuracy of creating 3D reconstruction in the future be-
sides the efficient algorithms. Modeling using neural network shows a great advantages [26], [8]. The defined
network will try to learn the shapes and will fill the occlusion region automatically.
ACKNOWLEDGEMENT
The author also would like to acknowledge Bina Nusantara University for the grant research funding.
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Computer vision based 3D reconstruction : A review (Ham Hanry)

2402 rISSN: 2088-8708
BIOGRAPHY OF AUTHORS
Hanry Ham is a lecturer and research assistant at Bina Nusantara University with Mas-
ter of Engineering from The Sirindhorn International Thai-German Graduate School of
Engineering in Thailand and German (2016). He obtained Bachelor Degree in Computer
Science from Bina Nusantara University (Indonesia) in 2014. His researches are in fields
of image processing, computer vision and computer graphics. He is affiliated with IEEE
as student member. Besides, he is also involved in student associations, and committee of
several competitions such as BNPCHS and ACM-ICPC Regional Asia Site.
Julian Wesley is a lecturer at Bina Nusantara University with Master of Computer Sci-
ence (M.TI.) major from Bina Nusantara University in 2016. His researches are in fields
of image processing, computer vision, and virtual reality. Besides, he is also work as a
technology consultant who focused on IT financial industries. He is leading a R&D team
in Emerio Indonesia and guide intern students from multiple universities in Indonesia.
Hendra is a lecturer at Bina Nusantara University. He was born in Tanjungpandan, 18
July 1992. He completed his bachelor degree in Bina Nusantara University on 2010.
Subsequently he obtained his master degree on 2018. Both degree are in Information
Technology. Now, he is working as a Software Engineer at a start-up company in Indone-
sia.
Int J Elec & Comp Eng, Vol. 9, No. 4, August 2019 : 2394 – 2402