3-D Point Clouds Cluster

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Presentation transcript:

3-D Point Clouds Cluster Yang Jiao

Outline Introduction Problem Methodology Result 3-D Point Cloud Challenge Goal Methodology Find Invariant Classify Signature Cluster Analysis Result Future Work References

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.

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

Goal 3D non-rigid objects recognition

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

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

Classify Signature Moment invariant insensitive to deformations

Classify Signature 3-D shape of gorilla and seahorse

Classify Signature Data plot shape of gorilla and seahorse

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

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

Result

Result Object Poses victoria horse seahorse gorilla david dog cat 1 6 3 4 7 5 pose2 2 pose3 pose4 pose5 Image

Future Work switching of eigenfunction values

Future Work '1' 'data/cat_1.obj' '2' 'data/david_9.obj' '7' 'data/gorilla_3.obj' '9' 'data/cat_2.obj' '7' 'data/gorilla_4.obj' '8' 'data/cat_3.obj' '10' 'data/dog_1.obj' '7' 'data/gorilla_5.obj' '3' 'data/cat_4.obj' '10' 'data/dog_10.obj' '7' 'data/gorilla_6.obj' '9' 'data/cat_5.obj' '10' 'data/dog_11.obj' '7' 'data/gorilla_7.obj' '3' 'data/cat_6.obj' '10' 'data/dog_2.obj' '7' 'data/gorilla_8.obj' '10' 'data/dog_3.obj' '7' 'data/gorilla_9.obj' '4' 'data/centaur_1.obj' '2' 'data/dog_4.obj' '4' 'data/centaur_2.obj' '10' 'data/dog_5.obj' '5' 'data/horse_1.obj' '4' 'data/centaur_3.obj' '10' 'data/dog_6.obj' '5' 'data/horse_10.obj' '4' 'data/centaur_4.obj' '10' 'data/dog_7.obj' '5' 'data/horse_2.obj' '4' 'data/centaur_5.obj' '10' 'data/dog_8.obj' '5' 'data/horse_3.obj' '4' 'data/centaur_6.obj' '10' 'data/dog_9.obj' '5' 'data/horse_4.obj' '5' 'data/horse_5.obj' '2' 'data/david_1.obj' '7' 'data/gorilla_1.obj' '5' 'data/horse_6.obj' '2' 'data/david_10.obj' '7' 'data/gorilla_10.obj' '5' 'data/horse_7.obj' '2' 'data/david_11.obj' '7' 'data/gorilla_11.obj' '5' 'data/horse_8.obj' '2' 'data/david_12.obj' '7' 'data/gorilla_12.obj' '5' 'data/horse_9.obj' '2' 'data/david_13.obj' '7' 'data/gorilla_13.obj' '2' 'data/david_14.obj' '7' 'data/gorilla_14.obj' '5' 'data/lioness_1.obj' '2' 'data/david_15.obj' '7' 'data/gorilla_15.obj' '5' 'data/lioness_2.obj' '2' 'data/david_2.obj' '7' 'data/gorilla_16.obj' '5' 'data/lioness_3.obj' '2' 'data/david_3.obj' '7' 'data/gorilla_17.obj' '5' 'data/lioness_4.obj' '2' 'data/david_4.obj' '7' 'data/gorilla_18.obj' '5' 'data/lioness_5.obj' '2' 'data/david_5.obj' '7' 'data/gorilla_19.obj' '5' 'data/lioness_6.obj' '2' 'data/david_6.obj' '7' 'data/gorilla_2.obj' '5' 'data/lioness_7.obj' '2' 'data/david_7.obj' '7' 'data/gorilla_20.obj' '5' 'data/lioness_8.obj' '2' 'data/david_8.obj' '7' 'data/gorilla_21.obj' '5' 'data/lioness_9.obj' '6' 'data/michael_1.obj' '2' 'data/victoria_10.obj' '6' 'data/michael_10.obj' '2' 'data/victoria_11.obj' '6' 'data/michael_11.obj' '2' 'data/victoria_12.obj' '6' 'data/michael_12.obj' '2' 'data/victoria_13.obj' '6' 'data/michael_13.obj' '2' 'data/victoria_14.obj' '6' 'data/michael_14.obj' '2' 'data/victoria_2.obj' '6' 'data/michael_15.obj' '2' 'data/victoria_3.obj' '6' 'data/michael_16.obj' '2' 'data/victoria_4.obj' '6' 'data/michael_17.obj' '2' 'data/victoria_5.obj' '6' 'data/michael_18.obj' '2' 'data/victoria_6.obj' '6' 'data/michael_19.obj' '2' 'data/victoria_7.obj' '6' 'data/michael_2.obj' '2' 'data/victoria_8.obj' '6' 'data/michael_20.obj' '2' 'data/victoria_9.obj' '6' 'data/michael_3.obj' '6' 'data/michael_4.obj' '11' 'data/wolf_1.obj' '6' 'data/michael_5.obj' '11' 'data/wolf_2.obj' '6' 'data/michael_6.obj' '11' 'data/wolf_3.obj' '6' 'data/michael_7.obj' '6' 'data/michael_8.obj' '6' 'data/michael_9.obj' '5' 'data/seahorse_1.obj' '5' 'data/seahorse_2.obj' '5' 'data/seahorse_3.obj' '5' 'data/seahorse_4.obj' '5' 'data/seahorse_6.obj' '2' 'data/victoria_1.obj'

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, 1988. [2] Daniel P Huttenlocher and Shimon Ullman. Object recognition using alignment. In Proceedings of the 1st International Conference on Computer Vision, pages 102–111, 1987. [3] Rongjie Lai and Hongkai Zhao. Multi-scale non-rigid point cloud registration using robust slicedwasserstein distance via laplace-beltrami eigenmap. arXiv preprint arXiv:1406.3758, 2014. [4] Jan Flusser, Barbara Zitova, and Tomas Suk. Moments and moment invariants in pattern recognition. John Wiley & Sons, 2009. [5] Lindsay I Smith. A tutorial on principal components analysis. Cornell University, USA, 51:52, 2002. [6] Joseph B Kruskal and Myron Wish. Multidimensional scaling, volume 11. Sage, 1978.

Thank you!