1. Classification of Large-Scale Shapes with Local Dissimilarities
Classification of Large-Scale Shapes with Local Dissimilarities
Xizhi Li University of Bremen, Germany cgvr.informatik.uni-bremen.de
CGI’17, June 2017, Yokohama, Japan

2. Motivation

3. 3D model classification basics
How to extract feature from this model ?
2D image ?
Implicit function ?

4. Previous works
LightField descriptors combine with Deep Learning [Bai & Zhou, 2016]:
Spectral-based shape analysis – ShapeDNA [Reuter, 2009]:
Histogram-based shape analysis [Rusu, 2008]
The first

5. Our contribution Extended the work of Wahl [wahl, 2003]
Add more shape descriptors to strength its discrimination
Parallel version of algorithm to adapt to large-scale model classification
Combine machine learning algorithm with histogram-based model classification
Our algorithm is robust dealing with noise and incomplete models compared with contemporary algorithms

6. Our shape descriptors Definition: AHD descriptor
Definition: AHD descriptor
α= ηis the Gaussian curvature
β= κis local normal perturbation γ= δ=

7. Our shape descriptors Parallel version Pseudo-Code: ——————————
Parallel version Pseudo-Code:
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\definecolor{blau}{rgb}{0.15,0.15,1}
————————
\color{blau} % besser zu lesen als ub_blue
d \leftarrow F(M) = F( x_0+1, y_0 + \tfrac{1}{2} ) = n_1 + \tfrac{n_2}{2}
—————
\color{blau}
t_1 = (\bar{A}_a \ominus O_a) \odot d'_a
—————————
t_\mathnormal{l} = \min\{ t_i^{\mathnormal{l}}, 1\}
A = \mathnormal{I} - \mathnormal{ \Lambda }

8. GPU VS CPU

9. Practice01:Asteroid classification
Experiment data origin from 3D asteroid catalogue website.
We selected 20 raw asteroids and utilizing possion disk sampling to extend the origin model into 1000 models.
Add random noise on the surface of each model. （to simulate the real situation）
The first is raw asteroid, the second one is sampling result. The third is noise asteroid.

10. Analysis PCA transform integrated all object‘s histogram into 3D space
Choose the optimum machine learning algorithm to segment histogram space.

11. Results:Asteroid classification

12. Practice02:NTU database classification
Experiment data origin from NTU database.
We selected 1,218 3D models are composed into 10 classes.
Some of the models are incomplete or integrated by several single parts.

13. Results:NTU database classification

14. Conclusions
Local shape descriptors enable our hybrid-shape descriptors to classify models with local dissimilarities
The parallel version of our algorithm can be adjusted to large-scale point clouds classification
Random forest can be used to improve histogram-based signal classification
Future challenges:
Shape classification is a balance between computation and accuracy. Histogram-based algorithm cannot achieve very high accuracy but robust to incomplete while noise model classification. In the future, we can improve both hands.
Incorporate with reinforcement learning

15. Thank you ! Q&A