3-d point clouds cluster

3-D Point Clouds ClusterYang Jiao

Outline

• Introduction

• 3-D Point Cloud

• Problem

• 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

pose1 1 6 3 4 1 7 5

pose2 1 6 3 4 1 7 2

pose3 1 6 3 4 1 7 5

pose4 1 6 3 4 1 7 5

pose5 1 6 3 4 1 7 5

Image

Future Work

• switching of eigenfunction values

Future Work• '1' 'data/cat_1.obj'

• '9' 'data/cat_2.obj'





•

• '4' 'data/centaur_1.obj'






•

• '2' 'data/david_1.obj'















•

• '10' 'data/dog_1.obj'











•

• '7' 'data/gorilla_1.obj'





















•

• '5' 'data/horse_1.obj'










•

• '5' 'data/lioness_1.obj'









• '6' 'data/michael_1.obj'




















•

• '5' 'data/seahorse_1.obj'





•

• '2' 'data/victoria_1.obj'














•

• '11' 'data/wolf_1.obj'



References

[1] Yehezkel Lamdan and Haim J Wolfson. Geometric hashing: A general and efficient model-based recognitionscheme. In ICCV, volume 88, pages 238–249, 1988.[2] Daniel P Huttenlocher and Shimon Ullman. Object recognition using alignment. In Proceedings of the1st International Conference on Computer Vision, pages 102–111, 1987.[3] Rongjie Lai and Hongkai Zhao. Multi-scale non-rigid point cloud registration using robust slicedwassersteindistance 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!

3-d point clouds cluster

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