1. 3-D Point Clouds Cluster
Yang Jiao

2. Outline Introduction Problem Methodology Result 3-D Point Cloud
Challenge
Goal
Methodology
Find Invariant
Classify Signature
Cluster Analysis
Result

3. 3-D Point Cloud data points in some coordinate system
hardware sensors such as stereo cameras, 3D scanners, or time-of-flight cameras, or generated from a computer program synthetically.

4. Challenge “posture” recognition 3 Dimension
non-rigid, non-linear transformation

5. Goal
 3D non-rigid objects recognition

6. Methodology 1. Find invariants from eigenfunctions
2. Using invariants as signature to classify different group
3. Cluster based on feature vector

7. Find Invariant intrinsic geometric analysis of underlying manifold
LB eigenfunction
Information of surface geometry
principal component analysis
Project
orthogonal axes with greatest variability

8. Classify Signature
Moment invariant
insensitive to deformations

9. Cluster Analysis Feature vector
Combine features from multiple dimension
Pairwise similarity information

10. Result Hierarchy cluster Point clouds group
Similarity between groups and group member

11. Result

12. Result Object Poses victoria horse seahorse gorilla david dog cat1 6 3 4 7 5
pose2 2
pose3
pose4
pose5
Image

13. Error
switching of eigenfunction values

14. References
[1] Yehezkel Lamdan and Haim J Wolfson. Geometric hashing: A general and efficient model-based recognition scheme. In ICCV, volume 88, pages 238–249, [2] Daniel P Huttenlocher and Shimon Ullman. Object recognition using alignment. In Proceedings of the 1st International Conference on Computer Vision, pages 102–111, [3] Rongjie Lai and Hongkai Zhao. Multi-scale non-rigid point cloud registration using robust slicedwasserstein distance via laplace-beltrami eigenmap. arXiv preprint arXiv: , [4] Jan Flusser, Barbara Zitova, and Tomas Suk. Moments and moment invariants in pattern recognition. John Wiley & Sons, [5] Lindsay I Smith. A tutorial on principal components analysis. Cornell University, USA, 51:52, [6] Joseph B Kruskal and Myron Wish. Multidimensional scaling, volume 11. Sage, 1978.

15. Thank you!