Results from SHREC 2006 bench mark and results using our methods
Ryutarou Ohbuchi

ohbuchi@yamanashi.ac.jp
www.kki.yamanashi.ac.jp/~ohbuchi

Ver.

2007.02

http://give-lab.cs.uu.nl/shrec/shrec2006/allRunfiles.html
Mean Average Precision(highly relevant)Rank Participant
RunNValue*Method A
0.6175
1 Shilane et al.
3 0.536588
2 Zaharia et al.
1 0.503491*Learned_MRSPRH
0.453852
3 Makadia et al.
2 0.486946
4 Shilane et al.
2 0.480416
5 Makadia et al.
1 0.474208
6 Daras et al.
1 0.447497
7 Papadakis et al.
10.4398 Shilane et al.
1 0.407198
9 Chaouch et al.
1 0.403972*Learned_MRAAD
0.384132*MRSPRH(KLD)
0.376057
10 Chaouch et al.
2 0.340585
11 Makadia et al.
3 0.334469*MRAAD
0.332241
12 Makadia et al.
4 0.315429*SRSPRH(KLD)
0.28863*SRAAD
0.237441
13 Laga et al.
1 0.232768
14 Laga et al.
2 0.22275
15 Jayanti et al.
2 0.170842
16 Jayanti et al.
3 0.15204
17 Jayanti et al.
1 0.143771

Mean Average Precision(relevant)Rank Participant
RunNValue*Method A
0.5966
1 Shilane et al.
3 0.519214
2 Zaharia et al.
1 0.509376
3 Shilane et al.
2 0.49619
4 Makadia et al.
2 0.436417
5 Makadia et al.
1 0.419766
6 Shilane et al.
1 0.416909*Learned_MRSPRH
0.410527
7 Daras et al.
1 0.395225
8 Papadakis et al.
1 0.389886*Learned_MRAAD
0.366346*MRSPRH(KLD)
0.355191
9 Chaouch et al.
1 0.354335*MRAAD
0.324646
10 Chaouch et al.
2 0.316559*SRSPRH(KLD)
0.317918
11 Makadia et al.
3 0.268425*SRAAD
0.260284
12 Makadia et al.
4 0.24939
13 Laga et al.
1 0.213509
14 Laga et al.
2 0.200307
15 Jayanti et al.
2 0.195591
16 Jayanti et al.
1 0.16448
17 Jayanti et al.
3 0.163372

Mean First Tier(highly relevant)Rank Participant
RunNValue*Method A
57.92%
1 Makadia et al.
244.77%2 Makadia et al.
143.78%*Learned_MRSPRH
43.12%
3 Daras et al.
142.75%
4 Papadakis et al.
141.86%
5 Shilane et al.
340.87%
6 Zaharia et al.
139.21%7 Chaouch et al.
138.13%8 Shilane et al.
237.91%*Learned_MRAAD
36.80%*MRSPRH(KLD)
34.93%
9 Chaouch et al.
2
32.15%*MRAAD
31.02%
10 Shilane et al.
130.97%11 Makadia et al.
329.99%12 Makadia et al.
428.43%*SRSPRH(KLD)
26.68%*SRAAD
24.55%
13 Laga et al.
1
24.11%
14 Laga et al.
2
23.13%
15 Jayanti et al.
2
17.28%
16 Jayanti et al.
315.95%17 Jayanti et al.
115.06%

Mean First Tier(relevant)Rank Participant*Method A
1 Makadia et al.
*Learned_MRSPRH
2 Makadia et al.
3 Shilane et al.
4 Papadakis et al.
5 Shilane et al.
6 Daras et al.
*Learned_MRAAD
7 Zaharia et al.
8 Chaouch et al.
*MRSPRH(KLD)*SRSPRH(KLD)*MRAAD
9 Chaouch et al.
10 Shilane et al.
*SRAAD
11 Makadia et al.
12 Makadia et al.
13 Laga et al.
14 Laga et al.
15 Jayanti et al.
16 Jayanti et al.
17 Jayanti et al.

RunNValue56.65%
240.55%39.98%
138.79%337.40%137.40%237.31%137.03%35.85%134.20%
134.08%32.84%
31.77%
31.38%
230.32%129.79%26.48%
325.41%424.53%222.23%121.98%220.17%118.17%317.07%

Mean Dynamic Average Recall
Rank Participant
RunNValue*Method A
0.6492
1 Makadia et al.
2 0.549863
2 Makadia et al.
1 0.540848*Learned_MRSPRH
0.527611
3 Daras et al.
1 0.524241
4 Chaouch et al.
1 0.500183
5 Papadakis et al.
1 0.495233
6 Shilane et al.
3 0.493715
7 Zaharia et al.
1 0.492473
8 Shilane et al.
2 0.487706*Learned_MRAAD
0.480721*MRSPRH(KLD)
0.463133*MRAAD
0.432029
9 Chaouch et al.
2 0.421568*SRSPRH(KLD)
0.398964
10 Shilane et al.
1 0.397066
11 Makadia et al.
3 0.392495
12 Makadia et al.
4 0.376673*SRAAD
0.336364
13 Laga et al.
1 0.326314
14 Laga et al.
20.3062
15 Jayanti et al.
2 0.267852
16 Jayanti et al.
3 0.237022
17 Jayanti et al.
1 0.230207
Mean Normalized Cumulated Gain @25
Rank Participant
RunNValue*Method A
0.67781 Makadia et al.
2 0.549824*Learned_MRSPRH
0.537946
2 Makadia et al.
1 0.52457
3 Daras et al.
1 0.523512
4 Shilane et al.
3 0.519081
5 Papadakis et al.
1 0.498866*Learned_MRAAD
0.488574
6 Zaharia et al.
1 0.486775
7 Shilane et al.
2 0.481823
8 Chaouch et al.
1 0.480672*MRSPRH
0.45199 Chaouch et al.
2 0.44134*MRAAD
0.438576*SRSPRH
0.391982
10 Shilane et al.
1 0.376791
11 Makadia et al.
3 0.370043
12 Makadia et al.
4 0.351979*SRAAD
0.34051
13 Laga et al.
2 0.310443
14 Laga et al.
1 0.306715
15 Jayanti et al.
2 0.259875
16 Jayanti et al.
3 0.220841
17 Jayanti et al.
1 0.212145

Mean Normalized Discounted Cumulated Gain @25
Rank Participant
RunNValue*Method A
0.69581 Makadia et al.
2 0.590602*Learned_MRSPRH
0.587107
2 Daras et al.
1 0.57909
3 Makadia et al.
1 0.576854
4 Shilane et al.
3 0.566407
5 Papadakis et al.
1 0.54827
6 Chaouch et al.
1 0.5469637 Zaharia et al.
1 0.539963*Learned_MRAAD
0.534729
8 Shilane et al.
2 0.534559*MRSPRH
0.510094
9 Chaouch et al.
2 0.485256*MRAAD0.479294
10 Makadia et al.
3 0.440445*SRSPRH
0.438447
11 Shilane et al.
1 0.430015
12 Makadia et al.
4 0.421528*SRAAD
0.3771213 Laga et al.
1 0.376507
14 Laga et al.
2 0.361473
15 Jayanti et al.
2 0.310237
16 Jayanti et al.
3 0.265014
17 Jayanti et al.
1 0.253685

Method A

Unpublished result.

Learned_MRSPRH

Unsupervised learning of a corpus (4000 models) using MRSPRH shapefeature (cosine distance) [Ohbuchi06b]

Learned_MRAAD

Unsupervised learning of a corpus (5000 models) using MRAAD shapefeature (cosine distance) [Ohbuchi06b]

MRSPRH

Multiresolution version of the SPRH [Wahl]. Alpha-MR method
proposed by [Ohbuchi03] is used.

MRAAD

Multiresolution version of the AAD, as desribed in [Ohbuchi03], butwith Cosine distance instead of L2 norm.

SRSPRH

Wahl's SPRH [Wahl03] for surface models by using qusi-monte Carlosampling of the surface gemetry [Ohbuchi06a].

SRAAD

Almost the same as the AAD [Ohbuchi03a, Ohbuchi05] but with Cosine
distance, instead of L2 norm.

References[Ohbuchi03a][Ohbuchi03b][Ohbuchi05][Ohbuchi06a][Ohbuchi06b][Wahl03]

Ryutarou Ohbuchi, Takahiro Minamitani, Tsuyoshi Takei, Shape
Similarity Search of 3D Models by using Enhanced Shape Functions,pp. 97-104, Theory and Practice of Computer Graphics 2003 (TP.CG.
Ryutarou Ohbuchi, Tsuyoshi Takei, Shape-Similarity Comparison of 3D
Shapes Using Alpha Shapes, Proc. Pacific Graphics 2003 , pp. 293-302,
October 8-10, Canmore, Canada.
Ryutarou Ohbuchi, Takahiro Minamitani, Tsuyoshi Takei, Shapesimilarity search of 3D models by using enhanced shape functions,International Journal of Computer Applications in Technology (IJCAT) ,Ryutarou Ohbuchi, Yushin Hata, Combining Multiresolution Shape
Descriptors for 3D Model Retrieval, Proc. WSCG 2006 , Plzen, Czech
Republic, Jan. 30~Feb. 2, 2006.
Ryutarou Ohbuchi, Jun Kobayashi, Unsupervised Learning from a
Corpus for Shape-Based 3D Model Retrieval, accepted, ACM MIR
2006, Santa Barbara, CA, U.S.A., Oct. 2006[Wahl03] E. Wahl, U. Hillenbrand, G. Hirzinger, Surflet-Pair-Relation
Histograms: A Statistical 3D-Shape Representation for RapidClassification, Proc. 3DIM 2003, pp. 474-481, IEEE Press, (2003).

* Note1: Learned_MRSPRH and Mehod_B are yet to be published.
* Note2: While [Ohbuchi06] describes combining multiple shape features by using Purity
analogue [Bustos04], none of our results above used purity based combination.
* Note3: All our papers listed above can be found at the following URL.
http://www.kki.yamanashi.ac.jp/~ohbuchi/publication.html