1. Semi-supervised Mesh Segmentation and Labeling
asd
Semi-supervised Mesh Segmentation and Labeling
Jiajun Lv, Xinlei Chen, Jin Huang, Hujun Bao
State Key Lab of CAD&CG, Zhejiang University

2. Motivation Recognition of Mesh Semantic Meanings Head Torso Upper arm
asd
Motivation
Recognition of Mesh Semantic Meanings
Head
Torso
Upper arm
Lower arm
Hand
Upper leg
Lower leg
Foot

3. asd
Motivation
3D Modeling

4. Related Work Geometric Structure Based Approaches
asd
Related Work
Geometric Structure Based Approaches
Drawbacks: No Suitable Geometric Feature
Data-driven Supervised Approaches
Drawbacks: Large Size of Training Dataset
Unsupervised Co-Segmentation Approaches
Drawbacks: Inferior to Supervised Method

5. Related Work Head Neck Torso Leg Tail Ear
asd
Related Work
Head
Neck
Torso
Leg
Tail
Ear
Learning 3d mesh segmentation and
labeling, KALOGERAKIS E., 2010
Unsupervised co-segmentation of a set
of shapes via descriptor-space spectral
clustering, SIDI O., 2011
Learning boundary edges for 3D-mesh
segmentation, BENHABILES H., 2011
Joint shape segmentation with linear
programming, HUANG Q., 2011

6. Semi-Supervised Learning
asd
Semi-Supervised Learning

7. Semi-Supervised Learning
asd
Semi-Supervised Learning

8. asd
Method

9. Key Technical Points Per-mesh Conditional Random Fields Model
asd
Key Technical Points
Per-mesh Conditional Random Fields Model
Incorporation of unlabeled mesh information with an entropy term
Learning Parameters with Virtual Evidence Boosting

10. Definition Unary Features Labeled Meshes Semi-supervised
asd
Definition
Unary Features
Labeled Meshes
Semi-supervised
Mesh Segmentation
Pairwise Features
Unlabeled Meshes

11. Per-mesh Conditional Random Fields Model
asd
Per-mesh Conditional Random Fields Model

12. Incorporation of unlabeled mesh information with an entropy term
asd
Incorporation of unlabeled mesh information with an entropy term
Information Gain:
Negative Conditional Entropy of the CRF on
Unlabeled Meshes
Greater Certainty

13. Learning Parameters with Virtual Evidence Boosting
asd
Learning Parameters with Virtual Evidence Boosting

14. asd
Learning Parameters with Virtual Evidence Boosting----Belief Propagation
Information about distribution of sending node
Information about which values recipient node should prefer

15. Learning Parameters with Virtual Evidence Boosting----LogitBoost
asd
Learning Parameters with Virtual Evidence Boosting----LogitBoost
1. Unary Energy Term
2. Pairwise Energy Term

16. Results and Discussion
asd
Results and Discussion

17. asd
Results
The segmentation and labeling results of our semi-supervised mesh segmentation algorithm
on the whole Princeton Segmentation Benchmark

18. asd
Results
Experimental results of the semi-supervised mesh segmentation method. For each kind of
dataset, the left column three are the labeled training dataset, and the right column three
are the segmented meshes.

19. asd
Results
Comparison of Supervised and Semi-supervised Approaches

20. asd
Results
Semi-supervised Approach with Different Labeled Training Meshes

21. asd
Robustness
Noise of the labeled set is inevitable, such as human mislabeling
Entropy term acts as a regularizer, avoiding over- fitting to training data

22. asd
Robustness
Comparison of Supervised and Semi-supervised Approaches on Inconsistent Labeled Data

23. Complexity of The Method
asd
Complexity of The Method
Training Complexity
Each Belief Propagation:
Each Boosting Iteration:
Labeling Complexity
All Belief Propagation:
All Boosting Iteration:
Time Consumption
Training: 7-12hours
Labeling: a few minutes

24. Limitation Manually tuning parameters Jagged and Disconnect Patches
asd
Limitation
Manually tuning parameters
Jagged and Disconnect Patches
Mesh with weak features

25. Future Work Hierarchical Models
asd
Future Work
Hierarchical Models
Partially Labeled Semi-supervised Mesh Segmentation

26. Thank You For Your Attention !
asd
Thank You For Your Attention !