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POINT-CLOUD COMPRESSION FOR VEHICLE-BASED MOBILE MAPPING SYSTEMS
USING PORTABLE NETWORK GRAPHICS
K. Kohira a, H. Masuda a, *
a Dept. of Mechanical Engineering and Intelligent Systems, The University of Electro-Communications,
1-5-1 Chofugaoka, Chofu, Tokyo, Japan - (keisuke.kohira, h.masuda)@uec.ac.jp
Commission VI, WG VI/4
KEY WORDS: Mobile Mapping System, point-clouds, lossless compression
ABSTRACT:
A mobile mapping system is effective for capturing dense point-clouds of roads and roadside objects．Point-clouds of urban areas,
residential areas, and arterial roads are useful for maintenance of infrastructure, map creation, and automatic driving. However, the
data size of point-clouds measured in large areas is enormously large. A large storage capacity is required to store such point-clouds,
and heavy loads will be taken on network if point-clouds are transferred through the network. Therefore, it is desirable to reduce data
sizes of point-clouds without deterioration of quality. In this research, we propose a novel point-cloud compression method for
vehicle-based mobile mapping systems. In our compression method, point-clouds are mapped onto 2D pixels using GPS time and the
parameters of the laser scanner. Then, the images are encoded in the Portable Networking Graphics (PNG) format and compressed
using the PNG algorithm. In our experiments, our method could efficiently compress point-clouds without deteriorating the quality.
1. INTRODUCTION
The vehicle-based mobile mapping system (MMS) is effective
for capturing 3D shapes of roads, buildings, and roadside
objects. Figure 1 shows an MMS, on which laser scanners,
cameras, GPSs and IMU are mounted. The MMS captures
point-clouds using laser scanners while running. Point-clouds of
urban areas, residential areas, and arterial roads are useful for
maintenance of infrastructure, map creation, automatic driving,
computer graphics, and so on.
When high-performance laser scanners are installed on the
MMS, the data size of point-clouds becomes very enormous. In
recent years, the performance of laser scanners has been greatly
improved. For example, the maximum data acquisition rate of
the Z+F Profiler 9012 is 1.016 million measurements per
second, and the one of the RIEGL VQ 450 is 0.55 million
measurements per second. When the MMS with a high-
performance laser scanner runs over a long distance, billions of
points are captured in one day. While dense point-clouds
maintain rich 3D information, the data volume often becomes
extraordinary large. Many hard disks are required to store large-
scale data, and it takes a long time to load the data from hard
disks on RAM.
In order to reduce the storage capacity and shorten loading time
for big data, data compression techniques are very important.
Data compression reduces data sizes by encoding measured data
in fewer bits.
In laser scanning, effective digits of 3D coordinates can be
reduced according to the measurement accuracy of the MMS. In
most cases, it is sufficient to record 3D coordinates up to 1 mm.
However, even if the number of significant digits is reduced, the
data size is still very large. Therefore, it is necessary to further
reduce the data size of point-clouds using coherency among
coordinates. 1
* Corresponding author
Several point-cloud compression methods have been proposed
so far. There are three typical types of compression methods for
point-clouds.
The first type of method is based on coherency between
consecutive points. LASZIP proposed by (Isenburg, 2013) has
been widely used to compress LiDAR data. This method
compressed organized points based on the observation that
coordinates of consecutive points are similar. However, it does
not use adjacency relationships on 2D space in cases of MMS
data. (He, et al. 2012) also compressed MMS data by encoding
consecutive points as second order differences.
The second type of method is space partition for unorganized
points. Most space partition methods for point-clouds are based
on the octree or the k-d tree. The both methods recursively
subdivide three-dimensional space and creates hierarchical
structure of points. In octree-based methods, (Peng et al., 2003)
and (Huang et al., 2006) proposed progressive lossless mesh
encoders. (Kammerl et al., 2012) compressed unordered point-
cloud streams by encoding differences between voxels.
(Elseberg et al., 2013) proposed octree-based data structure to
Figure 1. Mobile mapping system
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
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99
handle large points captured by terrestrial laser scanners.
(Hornung et al., 2013) proposed an octree map compression
method using probabilistic occupancy estimation. In kd-tree
based methods, (Gandoin et al., 2002) proposed a progressive
connectivity-coding algorithm using the kd-tree geometric coder
proposed by (Devillers et al., 2000). (Hubo et al., 2006)
proposed the Quantized kd-tree, in which points were encoded
using quantized split-plane positions.
The third type of method is based on 2D images for organized
points. If 3D points can be mapped on a 2D lattice image, point-
clouds would be more efficiently encoded. In terrestrial laser
scanning, it is well-known that a point-cloud can be mapped on
a 2D panoramic image if the scanner position is fixed. Even in
mobile mapping, some researchers converted points into images
for specific laser scanners. (Houshiar, 2015) mapped point-
clouds captured at the fixed positions onto panoramic depth
images, and encoded depth images using an image compression
method. For MMS data, (Kaess et al., 2003) proposed a
compression method for laser scanners that emit laser beams
line by line, such as the SICK LMS series. They aligned scan
lines in a lattice manner, and compressed them using an image
compression method. (Masuda et al., 2015) also proposed a
scan-line based compression method, in which a sequence of
points was encoded using the second-order differences. (Tu et
al., 2016) compressed raw point data of the Velodyne HDL-
64S2, which emits 64 laser beams simultaneously. They
converted a set of 64 scan lines into a panoramic image and
applied compression methods for images and videos. However,
these methods for mobile mapping data were developed only for
specific types of laser scanners. A majority of laser scanners for
MMSs, such as RIEGL VX and Z+F Profiler series,
continuously emit laser beams in a spiral manner, as shown in
Figure 2. In conventional methods, it is difficult to efficiently
compress MMS data captured by these popular laser scanners.
In this paper, we propose a novel image-based compression
method for point-clouds captured by MMSs. Our main
contribution is a novel compression framework for spiral-type
laser scanners, and a novel image-based compression method
for MMS data. In our method, point-clouds are projected onto a
2D lattice using GPS times and laser scanner parameters. Then,
we segment point-clouds on the 2D lattice into groups of
neighbor points, and compress each group of points using the
Portable Network Graphics (PNG). Our method can compress
point-clouds captured by MMSs without deteriorating the
quality.
2. MAPPING POINT-CLOUDS ONTO 2D IMAGES
2.1 Mapping 3D Points on 2D Lattice
Point-clouds captured by MMSs do not maintain explicit 2D
lattice structure, while point-clouds captured by TLSs can be
easily converted to panoramic images. Therefore, we first
consider a method for mapping point-clouds onto 2D images by
assigning the pulse number and the rotation number to each
point.
In spiral-type laser scanners, laser beams are continuously
emitted, as shown in Figure 2. Points are measured at the equal
time interval. We suppose that point-clouds include (𝑥,𝑦,𝑧)
coordinates, intensity values, and GPS times. A GPS time
indicates when the point was captured. RGB colors are often
added to points in post processing, but we do not discuss RGB
colour attributes in this paper, because compression of RGB
colours is trivial. Since point-clouds are converted into images
in our method, RGB colours can be simply compressed using
any image compression method.
In this paper, let {𝑝!} be the sequence of points, 𝑡𝑖 be the
elapsed time from the start of laser scanning until point 𝑝𝑖 is
measured. We also denote the rotation frequency of the laser
scanner as 𝑓, and the pulse repetition frequency of the laser
scanner as 𝜔. The rotation frequency is the number of rotations
Figure 2. Trajectory of the laser beams
Figure 3. Neighbour points on scan-lines
Figure 4. The rotation number
Figure 5. The phase number
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
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100
of laser beams per second, and the pulse repetition frequency is
the number of measurements per 1/𝑓 second.
Since the laser beam rotates once in 1/𝑓 second, we subdivide a
point-cloud every 1/𝑓 second. We call each segment as a scan-
line. As shown in Figure 3, neighbour points on the next scan-
line are captured 1/𝑓 second later.
In order to map a point-cloud on a 2D image, we assign the
phase number and the rotation number to each point. The
rotation number is the sequential number of scan-lines, as
shown in Figure 4. Points on the same scan-line have the same
rotation number. The rotation number of point pi can be
calculated as:
𝐽!=INT(𝑓∙𝑡!)
(1)
INT(𝑥) is the function that returns the integer part of 𝑥.
The phase number 𝐼𝑖 indicates the sequential order of point p!
on the scan-line. Figure 5 shows the phase numbers. Points with
the same phase number are ordered on the same column. The
phase number can be calculated as:
𝐼!=INT(𝜔∙FMOD 𝑡!,1/𝑓)
(2)
FMOD(𝑥,𝑦) is the function that returns the floating-point
remainder of 𝑥/𝑦.
Each point pi is mapped to (𝐼!,𝐽!) on the 2D lattice. If any point
is not mapped on a pixel, the pixel is marked as empty. Figure 6
shows an example of mapped points. In this figure, the vertical
axis is the phase number, and the horizontal axis is the rotation
number. The brightness of a pixel is the intensity value of each
point. Empty pixels are shown in blue colour.
2.2 Quantization of Coordinates
Coordinates measured by MMS include numerical errors caused
by laser scanners, GPS, calibration, and so on. Since the
accuracy of coordinate values is not very high, very high
resolution, such as sub millimeter, is not necessary.
To reduce the number of coordinate digits, we quantize point
coordinates according to the accuracy of the MMSs. In this
research, we set the quantization step as 1mm. This resolution is
sufficiently high considering the accuracy of MMS data.
2.3 Segmentation of 2D Lattice
Although we reduce the number of digits by quantizing the
coordinates, the number of digits is still very large. Therefore,
we represent coordinates as differences between neighbors to
make the data more compressible. In this paper, we consider
dividing the image so that the differences of 𝑥, 𝑦, and 𝑧 can be
encoded with 8 bits.
We segment the image into connected regions using the region
growing method. The point with the earliest GPS time is
selected as a seed point. Initially, the region contains only the
seed region. Then, neighbour points are added to the same
region only when the difference for each of 𝑥,𝑦,𝑧 is less than
256 mm. This process is repeated until neighbour points cannot
be added anymore. When there are unsegmented points, a new
seed point is selected from the remaining points, and the region
growing method is applied again.
Figure 7 shows the segmented image, in which connected
regions are shown in different colours. Figure 8 shows images
of connected regions. In our method, each region is converted
into the Portable Network Graphics (PNG) format.
3. POINT-CLOUD COMPRESSION USING
PORTABLE NETWORK GRAPHICS
3.1 Portable Network Graphics
PNG (W3C, 2003) is a patent free file format designed for
images. PNG supports lossless data compression, which can
completely reconstruct the original image from the compressed
data. The PNG algorithm consists of filtering and compression
stages. In the filtering stage, images are passed through delta
Figure 6. 2D lattice projected point-clouds
Figure 7. Connected regions
Figure 8. Images of connected regions
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
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filtering to make the image compressible. In the compression
stage, Deflate algorithm (Deutsch, 1996) is applied to the
filtered byte sequences. In this compression algorithm, LZ77
cording (Ziv et al., 1977) and Huffman cording are combined.
The PNG format supports several types of images. We use the
PNG format for RGB images with alpha channels to encode
point-clouds.
3.2 Differences of Coordinates
When connected regions are detected using the region growing
method, each pixel is added one by one. Figure 9 shows the
process of growing region. In this figure, when pixel 𝑃
! is
added to the connected region as the neighbor of pixel 𝑃
!, a
directed edge is defined from pixel 𝑃
! to pixel 𝑃
!. Since the all
nodes in the connected region are traversed during the region
growing, this graph becomes a directed spanning tree. When the
seed point is given, the spanning tree is uniquely reconstructed
according to the rule of region growing.
Once the directed spanning tree is generated, coordinate
differences can be calculated between the adjacent nodes in the
directed graph. As shown in Figure 10, the coordinate
difference ∆𝑝! is calculated between the seed and the neighbor
point on the spanning tree. Similarly, differences
∆𝑝!,∆𝑝!,⋯,∆𝑝! are calculated along the spanning tree. The
difference value is stored at each pixel. Difference values are
guaranteed to be 255 mm or less based on the rule of region
growing.
Coordinate 𝑝! can be reconstructed using the seed coordinate
𝑝!""# and difference values. Let Λ! be the index set of the
shortest path on the spanning tree from the seed to pixel 𝑖. Then
𝑝! can be calculated as:
𝑝!=𝑝!""# +∆
!∈!!
𝑝!
(3)
Therefore, in our method, the coordinate of the seed point and
the differences of other pixels are required to reconstruct all
original coordinates.
3.3 Encoding Point-Cloud
In our method, difference Δ𝑥!,Δ𝑦!,Δ𝑧! is stored at pixel 𝑖. We
encode them in the PNG format, which has RGB channels and
an alpha channel. In this format, 8 bits are allocated to each
channel of R, G, B, and alpha.
Figure 11 shows our encoding scheme. We as si gn three bits to
plus or minus signs of (𝑥,𝑦,𝑧), and store them in the alpha
channel as 0 or 1. The absolute values of (𝑥,𝑦,𝑧) are written in
RGB channels using 8 bits. The remaining 5 bits in the alpha
channel can be used optionally. In this paper, we use the 5 bits
for storing an intensity value, which is quantized to 25 = 32
levels.
The coordinate data in the PNG format is compressed and
stored in the file with .png extension. Figure 12 shows an
intensity image of a connected region and its PNG image. In
Figure 12(b), pixel colors are determined from coordinate
differences. We can reconstruct point coordinates and intensity
values from the encoded image. Figure 13 shows a point-cloud
reconstructed from a PNG image. Figure 13(a) encodes a point-
cloud equivalent to Figure 13(b).
The GPS time of each point can be also reconstructed, although
GPS times are not explicitly encoded in the file. In
measurement by a MMS, points are measured at the equal time
interval, and they are sequentially ordered on the image.
Therefore, the GPS time of each point can be determined using
the position on the image if the earliest GPS time of the seed
points is given. The GPS time 𝑇
! at pixel (𝐼!,𝐽!) can be
calculated using the GPS time 𝑇
!""# of the seed point as:
𝑇
!=𝑇
!""# +
𝐼!+𝜔𝐽!
𝜔𝑓
(4)
Figure 9. Spanning tree based on region growing
Figure 10. Difference values
Figure 11. Encoding coordinate difference
(a) Intensity image
(b) PNG image
Figure 12. Encoded image of a point-cloud
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
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In our method, the coordinate and GPS time of the seed are
required to reconstruct the original coordinates and GPS times.
In our implementation, these values are stored as the file name
of compressed data.
3.4 Encoding Fragmented Regions
In our method, each connected region is compressed as a PNG
file. When an image is segmented into connected regions, large
regions are generated from buildings, roads, and roadside
objects. On the other hand, small fragmented regions are also
generated from tree leaves, cables, and noises. In Figure 14,
while a large connected region is generated from a building,
many fragmented regions are generated from an electric cable.
In our method, while the compression efficiency for large
regions is very high, the efficiency is poor for fragmented
regions with few points. Therefore, we compress only large
regions by the PNG algorithm. In this paper, we regard regions
with 5 or less points as fragmented regions.
In Figure 15, fragmented regions are shown in red colour. In
residential areas, fragmented regions are mainly generated from
cables and trees.
Table 1 shows ratios of points in fragmented regions for point-
clouds A and B in Figure 16. Since many trees are included in
the point-cloud B, more fragmented regions are generated from
tree leaves. In both cases, the numbers of points in fragmented
regions are very small, because areas of tree leaves and cables
are much smaller than ones of roads and building walls,
In our method, points in fragmented regions are separately
encoded without using the PNG algorithm. In this paper, we
simply encode points in fragmented regions in a binary format
without compression, because the number of points in
fragmented regions is relatively very small.
4. EXPERIMANTAL RESULTS
We evaluated our compression method using two datasets A
and B, which were measured in a residential district in Japan
using Mitsubishi Electric MMS Type X. The mounted laser
scanner was Z+F Profiler 9012. As shown in Figure 16, point-
clouds A and B include houses, roads, utility poles, and so on.
Point-cloud B also includes many trees, and therefore a lot of
fragmented regions are generated from dataset B. The numbers
of points are 9.79 million in point-cloud A, and 6.54 million in
point-cloud B. For data compression, we used a PC with 3.1
GHz Intel core i5 CPU and 16 GB RAM.
We compared our method with the scan-line based compression
ASCII
Binary
Scan-Line
Based
Our
Method
Size
552.4 MB
319.5 MB
30.1 MB
23.0 MB
Ratio
100 %
57.8 %
5.4 %
4.2 %
Table 2. Compression results of point-cloud A
ASCII
Binary
Scan-Line
Based
Our
Method
Size
367.6 MB
213.2 MB
24.1 MB
18.4 MB
Ratio
100 %
58.0 %
6.6 %
5.0 %
Table 3. Compression results of point-cloud B
(a) PNG image
(b) Points
Figure 13. Image and points representing the same object
(a) Large region
(b) Fragmented region
Figure 14. Sizes of connected regions
(a) Electric cable (b) Tree leaves
Figure 15. Fragmented regions
Number of Points
Large Regions
Fragmented Region
Data A
9,762,923 (99.7%)
30,365 (0.3%)
Data B
6,448,844 (98.7%)
86,445 (1.3%)
Table 1. Number of points in large and fragmented regions
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
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103
method (Masuda et al., 2015). This method was designed for
MMS data, and compresses scan-lines using the second-order
differences. In our evaluation, coordinates, GPS times, and
intensity values were encoded in a file. The resolution of 𝑥, 𝑦,
and 𝑧 was 1 mm, and the intensity values were stored in 5 bits.
The results are shown in Table 2 and Table 3. In ASCII and
binary formats, data were stored without compression. In the
both datasets, our method has improved compression ratios by
more than 20% compared to the scan-line based compression
method.
Our method showed excellent compression ratios even when
many fragmented regions were generated. In our method, while
large regions are encoded using the PNG algorithm, fragmented
regions are stored without compression. In Table 4, data sizes
for both regions are described. In these datasets, the
fragmentation problem did not have significant effect on data
compression.
Figure 17 shows point-clouds that were reconstructed from
compressed data. Fragmented points are shown in red colour.
Figure 18-20 show details of reconstructed point-clouds. In all
cases, the original point-clouds could be faithfully reconstructed,
because our compression method is lossless after coordinates
are quantized to 1 mm resolution.
After compressed data are loaded from a hard disk, it has to be
decoded for use. Table 5 shows timing for loading and decoding
of compressed data. We compared with loading time of original
point-clouds represented in the ASCII format. The time for
loading and decoding compressed data was shorter than the
loading time of the original point-cloud.
Table 6 shows timing for compression process. We measured
calculation time for 2D mapping, segmentation, encoding to the
PNG format, and saving as a PNG file. In this evaluation, most
(a) Point-cloud A
(b) Point-cloud B
Figure 16. Original point-clouds
(a) Reconstructed point-cloud A
(b) Reconstructed point-cloud B
Figure 17. Reconstructed point-clouds
Dataset
PNG image
Fragmented points
A
22.4 MB (97 %)
0.6 MB (3 %)
B
16.5 MB (89 %)
1.9 MB (11 %)
Table 4. Data sizes of PNG images and fragmented points
Dataset
Compressed Data
Original Data
Load
Decode
Total
Load
A
1.0 s
4.0 s
5.0 s
13.9 s
B
0.9 s
2.6 s
3.5 s
9.1 s
Table 5. Timing for loading and decoding
Dataset
2D
Mapping
Segmenta
tion
Encoding
Writing
PNG
A
2.4 s
4.0 s
12.8 s
1.0 s
B
1.6 s
2.9 s
7.7 s
2.0 s
Table 6. Timing for compression process
Dataset
ΔX
ΔY
ΔZ
A
0.53
0.50
0.47
B
0.54
0.49
0.47
Table 7. Average coordinate errors (mm)
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
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104
computation time was used for segmentation and encoding
processes. The total computation time is 20.2 sec for point-
cloud A, and 14.2 sec for point-cloud B.
In our method, coordinates are quantized to 1 mm resolution.
Table 7 shows the average distance errors between the original
points without quantization and the reconstructed points. In the
both datasets, the average distance errors were about 0.5 mm.
Considering the measurement accuracy of MMSs, the resolution
of reconstructed point-clouds are sufficient.
5. CONCLUSION
We proposed a novel compression method for point-clouds
captured by a vehicle-based mobile mapping system. In our
method, point-clouds were projected onto pixels of an image
using the rotation number and the phase number, and the image
was segmented into connected regions. Then points in each
connected region were encoded to the PNG format, and
compressed using the PNG algorithm. In our experiments, our
method could achieve better compression ratios compared to the
scan-based compression method.
In future work, we would like to improve compression rates.
Since there are other lossless compression methods, such as
JPEG 2000, we would like to investigate other compression
schemes. In our method, it takes time to encode point-clouds.
We would like to improve our algorithm to shorten encoding
time. Currently, we evaluated our method only in residential
areas. We would like to evaluate our method using point-clouds
of highways and suburbs.
ACKNOWLEDGEMENTS
MMS data in this paper are courtesy of AISAN Technology
Co.Ltd. We would like to thank for their helpful support.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume IV-2/W4, 2017
ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China
This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
https://doi.org/10.5194/isprs-annals-IV-2-W4-99-2017 | © Authors 2017. CC BY 4.0 License.
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