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

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

asd Motivation 3D Modeling

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

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

Semi-Supervised Learning asd Semi-Supervised Learning

Semi-Supervised Learning asd Semi-Supervised Learning

asd Method

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

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

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

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

Learning Parameters with Virtual Evidence Boosting asd Learning Parameters with Virtual Evidence Boosting

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

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

Results and Discussion asd Results and Discussion

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

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.

asd Results Comparison of Supervised and Semi-supervised Approaches

asd Results Semi-supervised Approach with Different Labeled Training Meshes

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

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

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

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

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

Thank You For Your Attention ! asd Thank You For Your Attention !