1. Lior Shapira Tel Aviv University Shy Shalom Bar Ilan University Ariel Shamir The Interdisciplinary Center Daniel Cohen Or Tel Aviv University 1 H.Zhang Presented by Dana Silverbush

2.  Motivation  Why part analogies  The whole shape method (in a glimpse)  The Contextual Part Analogies Process  Partition  Measure  Determine distance  Results  Local vs. contextual  Robustness 2

3.  Why find part analogies  Analogies in a set of objects  Partial match queries  Instrument to carry information from one to many (semantic information, tags, deformation, operations). Dial dial 3

4.  Given q find similar s in set S  Common technique:  define f() and calculate for q and S f(q) S f(s) 4

5.  Given q find similar s in set S  Common technique:  define f() and calculate for q and S  Similarity: Two shape Sa, Sb, are similar if f(Sa) is close to f(Sb) in some sense, i.e. Dist(Sa,Sb) is small. - = ? 5

6.  Instead of using Dist(f(Sa),f(Sb) for the whole shapes, use it on sub parts!  Three main issues:  What sub-parts (segmentation)?  What descriptor f()?  What distance Dist(f(Sa), f(Sb))?  The process: Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 6

7.  The Shape Diameter Function (SDF)  Connects volume to surface  Ray Shooting & Averaging Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 7

8. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 8 for different scales Move to log-space: enhance delicate parts Notice the horn and the nose separate better

9. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 9  Fit Gaussian Mixture Model (GMM) of k Gaussians to the histogram of SDF.  Each face has a vector of length k signifying its probability to be in each cluster Normalized SDF faces

10. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 10  Employ an alpha expansion graph-cut algorithm to solve the k-way graph-cut Get start segmentation find min-cut to optimize energy function Swap alpha tags GMM Smoothing Incorporates edge length + angle Probability (face f belongs to cluster Xf)

11. Compare relative size of part Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 11 Normalized histogram of SDF values within the part The size of the part as a percentage of the whole model Compare volumetric measure

12. Compare relative size of part Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 12 Normalized histogram of SDF values within the part The size of the part as a percentage of the whole model

13. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 13 The part is not a separate shape We want to think of it in its context. A finger is closer to another finger than it is to a cylinder!

14. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 14

15. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 15

16. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 16 Context of a part -> the path between the node representing the part, and the root of the object tree

17. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 17 Context of a part -> the path between the node representing the part, and the root of the object tree

18. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 18 Context of a part -> the path between the node representing the part, and the root of the object tree

19. Partition Test Cluster Smooth Measure Define f() Distance Dist() Contextual distance 19 Context of a part -> the path between the node representing the part, and the root of the object tree

20. 20

21.  Comparing local signature to a context-aware distance measure 21

22.  Preforming part queries in several categories (human leg, armadillo leg and airplane wing) 22

23.  Five distinct partitioning were inserted, making part hierarchy different for each model  Query the dinopet hand returns matching hand from all dinopet variants 23

24.  Five distinct partitioning were inserted, making part hierarchy different for each model  Query the dinopet hand returns matching hand from all dinopet variants 24

25. 25

26. 26 All results marked with a yellow diamond have already been tagged ‘ head ’.

27.  Analogies based on parts  Utilizes Shape Diameter Function (SDF)  Segmentation + Distance  New Contextual Distance based on segmentation hierarchy 27

28. 28