Shape Retrieval Contest

Transcription

1 Shape Retrieval Contest 07 Remco Veltkamp, Frank ter Haar Utrecht University 1 SHREC 2006 Single track: polygon soup models Based on Princeton Shape Benchmark 30 new queries 8 participants 2 1

2 Seven tracks organized: SHREC 2007 Watertight models, organized by CNR-IMATI CAD models, organized by Purdue U Partial matching, organized by CNR-IMATI Protein models, organized by Freiburg U 3D face models, organized by Utrecht U Relevance feedback, organized by ITI Similarity measures, organized by Tokyo IT 3 SHREC 2007 Five tracks run: Watertight models, organized by CNR-IMATI 8 registrations, 5 participants CAD models, organized by Purdue U 9 registrations, 4 participants Partial matching, organized by CNR-IMATI 5 registrations, 2 participants Protein models, organized by Freiburg U 3 participants 3D face models, organized by Utrecht U 7 registrations, 3 participants 17 participants Proceedings: 4 2

3 SHape REtrieval Contest 07 Watertight Models Track Daniela Giorgi, Silvia Biasotti, Laura Paraboschi CNR IMATI 5 Data collection 400 watertight models to search in. Each model is used in turn as a query against all the other ones. Goal : for every query, retrieve the most similar objects, i.e., the whole class it belongs to. Object models represented by seamless surfaces, i.e. there are no defective holes or gaps 6 3

4 Data Collection 7 Participants Akgul et al. Bogazici University, Istanbul GETS Telecom Paris - CNRS Koc University, Istanbul Chaouch et al. INRIA Rocquencourtq Napoleon et al. Telecom Paris CNRS CDVP Dublin University Daras et al. ITI, Greece Tung et al. Telecom Paris CNRS 8 4

5 Results 1. Precision-Recall graphs 2. Average rank, computed averaging the retrieval ranking of all items. Here, the best value is ( )/20= Last place ranking L=1-(Rank-n)/(N-n), where Rank is the position of the last retrieved item, n=20, N=

6 11 4. Percentage of success of First and Second item : PF=100% for every participant, while Akgul Chaouch Napoleon Daras Tung PS 93.97% 92.81% 95.87% 93.33% 97.68% 5. Average dynamic recall (best value = 1) : Akgul Chaouch Napoleon Daras Tung ADR (Discounted) cumulated gain vector, which takes in account the position of all the relevant retrieved items. D. Giorgi, S. Biasotti, L. Paraboschi, SHape REtrieval Contest 07: Watertight Model Track, in this Proceedings. 12 6

7 13 SHREC 07 Cad models track By Yagna Kalyanaraman and Karthik Ramani Purdue University

8 Why engineering models track? Engineering parts high genus, rounding features (fillets, chamfers) presence of internal structure. They are closed watertight volumes. Engineering models can be parts or assemblies. For example, a wheel can be a part where as a bike is an assembly. the engineering context is unique in part families and parametric models, i.e. models differ by relative dimensions of various local geometries, are common. Focus on engineering parts and the search tasks in an engineering context. 15 Engineering Shape Benchmark Established dataset of engineering models 867 parts, models in neutral format Classification 3 Super-classes: flat-thin walled, rectangularcubic prism, solids of revolution 45 classes S. Jayanti, Y. Kalyanaraman, N. Iyer and K. Ramani, Developing An Engineering Shape Benchmark For Cad Models Computer-Aided Design, Volume 38, Issue 9,, September 2006, Pages

9 Query set All the models in the query set except six were from ESB four main search conditions in the engineering context Subdivided/Decimated: the different triangulation parameters for the same model Parametric Variation: A class or family of parts has the same overall shape but different parameters Slightly modified: the small holes and features in a model are ignored. (level of detail) Partial Shape: Sometimes the user might be interested in a specific portion of the shape of a part. These may be advanced level tasks for the shape search engines at this time. 17 Participants 10 registered, 4 submissions Petros Daras et al from ITI (5 runs) Thibault Napoleon from ENST-TSI (2 runs) Tony Tung et al from Telecom Paris (4 runs). Ryutarou Ohbuchi et al from University of Yamanashi (2 runs) 18 9

10 Evaluations top 100 results Best: Kobayashi & Ohbuchi from Univ. Yamanashi 19 More Information CAD models track, results Engineering Shape Benchmark PRECISE Lab

11 SHape REtrieval Contest 07 Partial Matching Track S. Marini, L. Paraboschi, S. Biasotti CNR Istituto di Matematica Applicata e tecnologie Informatiche 21 Data and Query sets Dataset : 400 watertight models. Queryset : 30 hybrid models, obtained by combining subparts of models in the dataset. Goal : for each query Q, retrieve the models (and the class they belong to) which share similar subparts with Q

12 Dataset Collection 23 Query set collection 24 12

13 (Non-) Relevant items Highly relevant : classes whose models compose Q. Marginally relevant : models reasonably similar to Q. Non-relevant : all the other ones. Q 25 Participants N. Cornea, F. Demirci (two runs) Rutgers University Utrecht University Biasotti, Marini et al., (two runs) CNR-IMATI, Italy 26 13

14 Performance measure and results Normalized Discounted Cumulated Gain Vector : it considers the positions of highly and marginally relevant items in the ranked lists. Highly relevant items Highly and marginally relevant items

15 SHREC 07 Protein Track Maja Temerinac, Marco Reisert and Hans Burkhardt LMB, University of Freiburg, Germany 29 Motivation Proteins are linear sequences of amino acids which fold into three dimensional structures. The three dimensional structure of a protein is closely linked to its function. By finding similar 3D protein structures, their function and evolutionary linkage can be determined 30 15

16 Task Classify unknown protein domains to predefined SCOP domains Only the ATOM part (3D atom coordinates) of the PDB file is given Participants were able to train their feature extraction algorithms on a provided data set The participants were provided with a set of 30 unknown protein domains 31 Training Data Set 633 protein domains divided into 27 folds according to their SCOP ids Protein structures can be classified hierarchically: Highest level: Similiar secondary strucutres (SCOP class) Finest level: Same major secondary structures in the same arrangement with the same topology(scop fold) 32 16

17 Evaluation A ranked list was submitted for each query protein with the similarity to each protein from the training set The Nearest Neighbor was considered for each protein 2 Points were assigned for the correct SCOP class 1 Point for the correct SCOP fold 33 Testing Data Set 34 17

18 Participants Purdue University, USA B. Li, Y. Fang, K. Ramani, D. Kihara ITI, Greece P. Daras, V. Tsatsaias LMB, Germany M. Temerinac, M. Reisert, H. Burkhardt 35 Results More results on the homepage:

19 SHREC 2007: Shape Retrieval Contest of 3D Face Models Frank B. ter Haar and Remco C. Veltkamp Utrecht University 37 Retrieval Contest of 3D Face Models Shape Retrieval Contest of 3D Face Models Take all relevant faces into account Generally applicable Different face models (not frontal scans only): Different scanners, poses, resolutions, post-processing Short term How can organizers obtain this data? How can participants handle this data? frontal scan / head reconstuction / head scan / full body scan 38 19

20 3D Morphable Face Model 3D Morphable Face Model (MFM by Vetter and Blanz) Build from 200 Cyberware TM Full Head & Face 3D scans Face morphing using 100 weights for principal shape components Assume proper fit to all types of data possible 39 3D Morphable Face Model Advantage MFM in general: Complete face only No holes Good topology Same data density Advantage for retrieval contest Automatic creation of database Automatic creation of relevant faces No pre-processing 40 20

21 Contest Data (1) Initial database 1000 random instances of MFM Snapshots online Registration & Query submission 10 triples per participant as Face A Face Q Face B 64 unique triples selected 41 Contest Data (2) Given triple Face A Face Q Face B Automatic generation of relevant faces Use morphing 4 highly relevant faces 4 marginally relevant faces 42 21

22 Contest Data (3) Final database 1000 initial faces 512 new relevant faces 4 duplicate faces check consistency Randomly rotated Result submission Runfiles with 64 ranked list of entire database Results from three participants: 10 runfiles 43 Results Some evaluation measures Identical first Random rotation problem Mean Average Precision Highly Relevant (MAPH) Mean Average Precision All Relevant (MAPR) Mean Average Dynamic Precision (MADP) 44 22

23 Summary Database Over 1500 MFM Objective Retrieve relevant faces Result Pose normalization is difficult Further research Face recognition/retrieval different scan data Pose normalization? Rotation invariant matching? 45 23