Transcript A case study for tele-immersion communication applications

A case study for Tele-Immersion communication
applications: From 3D capturing to rendering
Dimitrios Alexiadis, Alexandros Doumanoglou, Dimitrios Zarpalas, Petros Daras
Information Technologies Institute, Centre for Research and Technology - Hellas
Introduction – Problem Definition
Tele-Immersion (TI) technology aims to enable
geographically-distributed users to communicate
and interact inside a shared virtual world, as if they
were physically there [1].
Key technology aspects:
1) Capturing and reconstruction of (3D) data,
2) (3D) data compression and transmission,
3) Free-view-point (FVP) rendering
Substantial factors for user’s immersion:
1) Quality of (3D) generated and presented content,
2) Data exchange rates between remote sites,
3) Wide navigation range (full 360o 3D coverage)
3D Data representations [2]:
a) (Multi-view) Image-based modeling,
b) (Multi-view) Video+Depth,
c) 3D geometry-based representations
Contribution: We propose and study in detail a 3D
geometry representation-based TI pipeline, as a
reference for people working in the field.
The reasons for selecting 3D geometry-based
representations are: (a) Straightforward realization
of scene structuring and navigation functionalities
(e.g. collision detection); b) Wide range of FVP
coverage with relatively few cameras; c) Less
computational and network load for multi-party
systems; d) Potential benefit from the capabilities of
future rendering systems (holography-like).
Related work: [1] Multiple stereo rigs and view
synthesis at the receiver side; [3] Multiple Kinects,
but without studying compression/transmission;
[4]: Multiple Kinects, but compression is for the
specific reconstruction method[5]; [6]: Based on
image representations.
Tele-Immersion Platform
Systems overview
An overview of TI communication chain is given in
Fig. 1: Multiple calibrated RGB-Depth sensors
capture the user and the depth data are used to
reconstruct her/his 3D shape, in the form of a
triangle mesh. The mesh resolution is adaptable,
(affecting the reconstruction details and visual
quality) and therefore the whole chain is scalable .
Data representation: Time-varying a) geometry
(vertex positions and normals), b) additional vertex
attributes (see below), c) connectivity, d) textures.
Vertex attributes: [ID1, ID2, w1, w2], with w1+w2=1.
These are the texture IDs and the corresponding
weights, to be used for a vertex with textureblending rendering – They are estimated at the
capturing site, and used at receiver/rendering site.
Multiple RGB-D capturing
Capturing system: Five kinect1 sensors that
provide 360o coverage of the whole human body.
Calibration: Internal calibration using the method of
[7] and external calibration with a chessboardbased, all-to-all, custom calibration approach.
Real-time 3D reconstruction of humans
1) Preprocessing: a) Foreground segmentation;
b) Depth-maps denoising with bilateral fitlering;
c) Estimation and voxelization of the bounding box.
2) For each camera, extraction of a) the normal map
and b) a depth-confidence map, based on the
closeness of a pixel to the object’s boundary.
3) A volumetric 3D reconstruction method: The 3D
surface is implicitly defined by a volume function
V(X), which is zero at the surface of the object.
More specifically, [8], [9] are used:
The final triangle mesh surface is extracted using
Marching Cubes for V(X)=0.
A GPU implementation is used in the experiments.
Appropriate weights wk:
Express the “quality” of depth measurements. It
reduces with the a) Angle between line of sight and
surface normal, b) Closeness of a depth’s pixel to
the object’s boundary.
4) Texture mapping: The same kind of weights is
used for the texture mapping (vertex attributes).
ID1, ID2 correspond to the two largest weights.
Fast 3D data compression
Geometry compression: Geometry information,
along with the vertex attributes (ID1, ID2, w1), are
compressed using the OpenCTM mesh compression
scheme (http://openctm.sourceforge.net/).
The texture IDs are quantized with precision equal
to 1. The weights w1,k are quantized with a large
step (0.2), without affecting the visual quality.
Texture compression: The texture videos of the
mesh are separately compressed using H.264 video
coding. The Intra-frame period was set equal to 10.
Fig 3. Visual-quality and frame-rates, evaluation Diagrams
Fig 4. Time measurements for the whole capture-to-render chain
Experimental study - Discussion
We study on the performance of the end-to-end
TI chain with respect to its parameters, in terms of
visual quality and timing measurements.
The corresponding metrics are a) PSNR (obtained for
a large number of virtual views), and b) Frame rate
(fps) / delay.
Figure 3(a): Quality of the reconstruction with
respect to resolution level. The volume resolution is
2rx 2rx 2r voxels. As reference for PSNR, r=7 is used.
Figure 3(b): Reconstruction rate with respect to
resolution level. Based on Fig. 3(a),(b) a good
compromise is r=6, which is used below.
Figure 3(c): Impact of H.264 compression to the
visual quality. Q refers to the quantization parameter
of base quality layer. Q=28 (30dB) is selected as a
good compromise and used below.
Figure 3(d): Impact of the OpenCTM absolute vertex
precision d (quantization parameter), to the visual
quality. A vertex precision of d = 8mm, which
corresponds to 4.8bpvf and a PSNR 22dB, was
chosen after subjective evaluation (see Fig. 2).
Fig 2. An example frame
under different reconstruction and compression settings
Figure 4: Performance of the whole chain, in terms
of the frame rate at the receiver and overall delay
between capturing and rendering, with respect to
the line-speed. Two different CTM entropy coding
(lossless) levels are considered.
Drawn conclusions:
a) For slow lines (<5Mbps), transmission is the
bottleneck. The rate and the delay are respectively
proportional and inv. proportional to the line speed.
b) For fast lines, compression is the bottleneck. It is
preferable to have inefficient, but fast compression.
c) For slow lines (<5Mbps), the total experience is
expected to be poor for the set quality standards.
Figure 1. An overview of the studied Tele-Immersion chain, from capturing to rendering.
Contact
Petros Daras and Dimitrios Alexiadis
Email: [email protected] and [email protected]
Centre for Research and Technology – Hellas,
Information Technologies Institute, Visual Computing Lab
Website: http://vcl.iti.gr/
Phone: +30 2310 464160 (ext. 277)
References
1.
Supported by
2.
3.
4.
5.
6.
7.
8.
9.
R. Vasudevan, G. Kurillo, E. Lobaton, T. Bernardin, O. Kreylos, R. Bajcsy, and K. Nahrstedt, “High quality visualization for geographically distributed 3D teleimmersive
applications,” IEEE Trans. on Multimedia, vol. 13(3), June 2011
A. Smolic, “3D video and free viewpoint video-From capture to display,” Pattern Recognition, vol. 44, no. 9, Sep. 2011
A. Maimone and H. Fuchs, “Encumbrance-free telepresence system with real-time 3D capture and display using commodity depth cameras,” in 10th IEEE ISMAR
2011
R. Mekuria, D. Alexiadis, P. Daras, and P. Cesar, “Real-time encoding of live reconstructed mesh sequences for 3D tele-immersion,” in Intern. Conf. on Multimedia and
Expo, 2013
D. S. Alexiadis, D. Zarpalas, and P. Daras, “Real-time, full 3D reconstruction of moving foreground objects from multiple consumer depth cameras,” IEEE Trans. on
Multimedia, vol. 15, no. 2, pp. 339–358, 2013
B. Dai and X. Yang, “A low-latency 3D teleconferencing system with image based approach,” in 12th Int. Conf. on VR Continuum and Its Appl. in Industry, 2013
D. Herrera, J. Kannala, and J. Heikkila, “Joint depth and color camera calibration with distortion correction,” IEEE Trans. Pattern Anal Mach. Intell., vol. 34, 2012
D. Alexiadis, D. Zarpalas, and P. Daras, “Real-time, realistic full-body 3D reconstruction and texture mapping from multiple kinects,” in 11th IEEE IVMSP, Korea, 2013
B. Curless and M. Levoy, “A volumetric method for building complex models from range images,” in Proc. SIGGRAPH, 1996