1. 3D ShapeNets: A Deep Representation for Volumetric Shapes Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, Jianxiong Xiao -- Presented by Yinan Zhao

2. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

3. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

4. Generative vs Discriminative GenerativeDiscriminative Graph from [1] Fix this when generating a sample for a category Bi-directional One-directional

5. Generative vs Discriminative Confusion Matrix (Generative)

6. Generative vs Discriminative Generative: 43.39% Discriminative: 83.54% Generative Vary significantly across categories Hard categories: Bookshelf, flower pot(plant), vase(bottle), table(desk) Failure: Complex shape, Semantically similar Discriminative Fine-tuned for classification Better for classification

7. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

8. Shape Sampling Bathtub GT Sample

9. Shape Sampling Desk GT Sample

10. Shape Sampling Monitor GT Sample

11. Shape Sampling Bathtub +Desk Hollow

12. Shape Sampling Some shapes are reasonable A few hot hit samples regardless of category Overfitting Bathtub +Desk In some samples, features of bathtub and desk are combined

13. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

14. Gibbs Sampling Two variants of Gibbs sampling Top layers All layers Graph from [1]

15. Gibbs Sampling Top Layers: Do gibbs sampling on the top associative memeory, and propagate it down All Layers: Sampling process involves all layers in way that mimics the completion process(up down up down)

16. Gibbs Sampling Two ways of Gibbs sampling Top layers All layers

17. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

18. Visualization of Latent Feature Latent feature is refered to as 1200D feature (0-1 vector) in generative model How does each dimension affect sampling shape? Initialize with all zeros. Add each 1 one by one. Forward: start from small index Backward: start from large index

19. Visualization of Latent Feature Forward

20. Visualization of Latent Feature Backward

21. Visualization of Latent Feature Dimensions are correlated. Hard to observe individual effect.

22. Outline Generative vs Discriminative Category-based 3D shape sampling Comparison of variants of Gibbs sampling Visualization of latent feature Shape completion

23. Shape Completion Given depth from single view, infer 3D shape

24. Shape Completion

25. NYU dataset (a)(b) Image from [2]

26. Shape Completion Completion from (a)

27. Shape Completion Completion from (b)

28. Reference [1] Wu, Zhirong, et al. "3d shapenets: A deep representation for volumetric shapes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [2] Silberman, Nathan, et al. "Indoor segmentation and support inference from RGBD images." Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012. 746-760.

29. Thank s!