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Cutting-Plane Training of Non-associative Markov Network for 3D Point Cloud Segmentation

Cutting-Plane Training of Non-associative Markov Network for 3D Point Cloud Segmentation. Roman Shapovalov , Alexander Velizhev Lomonosov Moscow State University. Hangzhou, May 18, 2011. Semantic segmentation of point clouds. LIDAR point cloud without color information

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Cutting-Plane Training of Non-associative Markov Network for 3D Point Cloud Segmentation

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  1. Cutting-Plane Training of Non-associative Markov Network for 3D Point Cloud Segmentation Roman Shapovalov, Alexander Velizhev Lomonosov Moscow State University Hangzhou, May 18, 2011

  2. Semantic segmentation of point clouds • LIDAR point cloud without color information • Class label for each point

  3. System workflow featurecomputation CRFinference segmentation graphconstruction

  4. Non-associative CRF node features edge features point labels • Associative CRF: • Our model: no such constraints! [Shapovalov et al., 2010]

  5. CRF training • parametric model • parameters need to be learned! CRFinference

  6. Structured learning [Anguelov et al., 2005; and a lot more] • Linear model: • CRF negative energy: • Find such that …

  7. Structured loss • Define • Define structured loss, for example: • Find such that …

  8. Cutting-plane training • A lot of constraints (Kn) • Maintain a working set • Add iteratively the most violated one: • Polynomial complexity • SVMstruct implementation [Joachims, 2009]

  9. Results [Munoz et al., 2009] Our method

  10. Results: balanced loss better than Hamming loss

  11. Results: RBF better than linear

  12. Results: fails at very small classes 0.2% of trainset

  13. Analysis • Advantages: • more flexible model • accounts for class imbalance • allows kernelization • Disadvantages: • really slow (esp. with kernels) • learns small/underrepresented classes badly

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