3D Video Over Time. Presented on by. Daniel Kubacki
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1 3D Video Over Time Presented on by Daniel Kubacki Co-Advisors: Minh Do & Sanjay Patel This work funded by the Universal Parallel Computing Resource Center
2 2
3 What s the BIG deal? Video Rate Capture Digital Michelangelo Project Camera and/or object do not need to be static Simple Equipment that will be available to the average consumer Marc Levoy, et. al. The digital michelangelo project: 3d scanning of large statues. SIGGRAPH 00, pages , 3
![[Do] Computer Vision [Huang] Meshed](/docs-images/80/81151193/images/4-2.jpg "Surface Reconstruction [Hart]")
![Infrastructure [Patel, Nahrstedt]](/docs-images/80/81151193/images/4-4.jpg "Each component is an existing")
4 AvaScholar Block Diagram Range Image Integration & Segmentation [Do] Computer Vision [Huang] Meshed Surface Reconstruction [Hart] Infrastructure [Patel, Nahrstedt] Each component is an existing collaborative project that can benefit from better parallel programming upcrc.illinois.edu 4
5 Live Demo PMD Technologies 204 x 204 pixel resolution Frame rate up to 60 fps Measures Depth, Intensity, and Amplitude Applications Segmentation and tracking Real time 3D modeling Video relighting Free viewpoint video 5
6 Limitations Resolution Noise Depth
7 Utilize 3D Over Time Registration Assume camera or object to be non-static Requires alignment Integration How to accumulate data? Raw set of points Set of cleaned surface points Set of control points Implicit model 7
8 State of the Art - Registration Iterative Closest Point Point Selection Point Matching Rejection Weighting Optimization 8
9 Point Matching Closest 9
10 Point Matching Projection 10
11 State of the Art - Integration Robust Implicit Moving Least Squares 11
12 Implicit Moving Least Squares Non-Robust version. Can be thought of as placing a weighted plane function at each input point and summing the weighted distances from each plane. Signed Distance Function 12
13 Register to a Model ICP is for point set to point set registration We would like to register a point set to a model Thus, given a model and a new point set Register point set to model Update model 13
14 Register to a Signed Distance Model where is the closest point on the surface with signed distance function 14
15 Advantages Denoising 15
16 MeshLab Visualizations 16
17 Timing Computations to evaluate and are of order. Where N is the number of points to evaluate and M is the number of model points On an i7 laptop core in Matlab this takes 7.2secs for 5000 points. Which is 14ms/pt. 17
18 Making it Fast Bottlenecks Storage/Memory Storing a large number of points 5,000 pts/frame * 20 frames/s * 60 s/min = 6,000,000 pts/min Computation Evaluating IMLS 18
19 Sample IMLS in 3D Grid Advantages Do not need to store points Evaluation of f(x) in constant time with interpolation Grid can be adaptive sampling for non-uniform distribution of 3D points Disadvantages Registrations can not be adjusted given more data More initial computations 19
20 Thoughts on Parallelization Required Computations Preprocess Registration Integration Gather Oriented Binning Opportunities Challenge: do in < 50 ms 20
21 Conclusion Research into real time algorithms that can denoise and increase the resolution of 3D videos are essential for applications beyond simple segmentation and tracking. This work is in conjunction with John Hart s AvaScholar project which aims to utilize 3D video in a real time environment. 21
22 References Stanford University Computer Graphics Laboratory, The Stanford 3d scanning repository, May [Online]. Available: S. Rusinkiewicz and M. Levoy, Efficient variants of the ICP algorithm, in Third International Conference on 3D Digital Imaging and Modeling (3DIM), Jun [Online]. Available: C. Shen, J. F. O Brien, and J. R. Shewchuk, Interpolating and approximating implicit surfaces from polygon soup, in Proceedings of ACM SIGGRAPH ACM Press, Aug. 2004, pp [Online]. Available: C. Oztireli, G. Guennebaud, and M. Gross, Feature preserving point set surfaces based on non-linear kernel regression, Computer Graphics Forum, vol. 28, no. 2, p , [Online]. Available: R. Kolluri, Provably good moving least squares, ACM Trans. Algorithms, vol. 4, pp. 18:1 18:25, May [Online]. Available: M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, and D. Fulk, The digital michelangelo project: 3d scanning of large statues, in Proceedings of the 27th annual conference on Computer graphics and interactive techniques, ser. SIGGRAPH 00. New York, NY, USA: ACM Press/Addison-Wesley Publishing Co., 2000, pp [Online]. Available: 22
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