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Ohbuchi Laboratory
Graduate School of Engineering, University of Yamanashi, Yamanashi, Japan. |
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大渕 竜太郎
Ryutarou Ohbuchi
Professor
Computer Science Department
University of Yamanashi
4-3-11 Takeda, Kofu-shi, Yamanashi-ken
400-8511, Japan
ohbuchi *a t* yamanashi d0t ac d0t jp |
Backcountry skiing
Nishi-adzuma peak, Fukushima, Japan. |
Ryutarou Ohbuchi
Professor
Computer Science Department, University of Yamanashi
I teach at the Computer Science Department (or, Computer
and Media Engineering Department) of the faculty of
engineering, University of Yamanashi.
Ohbuchi Lab. Internal Page.
My publication list and resume
Research
My Research
講義 Courses
2012年度 前期 Spring semester
計算機アーキテクチャ I 2012 (Computer Architecture I)
ビジュアルコンピューティング 2012 (Visual Computing)
組み込みシステム概論 2012 （大渕担当分）(Intro. to Embedded Systems (Graduate Course, omnibus))
2012年度 後期 Fall semester
計算機アーキテクチャ II 2012 (Computer Architecture II)
科学技術英語演習 2012 (Scientific and Technical English Practices)
意味的マルチメディア処理特論 2012 (Semantic Multimedia Processing (Graduate Course))
News and updates
2011年度
SHREC 2012 Shape Retrieval Contest (February, 2012)
Shape Retrieval Contest based on Generic 3D Dataset
We placed 1st in this track. Mr. Tomohiro Yanagimachi pushed the effort.
Details of the performance evaluation results, with recall-precision plots
and a table of performance indices are found at an web site at NIST. Our best performing variation, named Yanagimachi(DG1SIFT), in
the evaluation result, used the BF-DSIFT combined a few more features, combined with distance metric learning based
on slightly modified Manifold Learning. However, least powerful of our three entries, BF-DSIFT alone combined with slightly modified Manifold Learning, wins over the others.
2nd place went to Mr. A. Tatsuma and Prof. M. Aono, who are friend of myself. Prof. Aono and I worked together for IBM Research
Lab. in Tokyo.
我々のチームが1位！柳町 知宏さんが中心になって頑張ってくれました．詳細はこのページのグラフや表を見てください．一般的3次元モデルを対象とした本部門で，古屋 貴彦さん（2010年3月卒業）が開発したBF-DSIFT手法に改良型Manifold Learningを加えただけのYanagimachi(DSIFT）で1位．さらに，これにBF-GSIFT，１SIFTなどの特徴を組み合わせたYanagimachi（DG1SIFT）ではさらに他を引き離しました．
2位も日本チーム！2位になった豊橋技術科学大学のTatsuma（立間さん）さんと青野 雅樹教授は大渕のお友達．（青野さん大渕はIBM東京基礎研究所で同じグループでした！）
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B. Li, A. Godil, M. Aono, X. Bai, T. Furuya, L. Li, R. López-Sastre, H.
Johan, R. Ohbuchi, C. Redondo-Cabrera, A. Tatsuma, T. Yanagimachi, S. Zhang,
SHREC’12 Track: Generic 3D Shape Retrieval, to appear, Proc. Eurographics Workshop on 3D Object Retrieval, May , 2012. (PDF) |
Sketch-Based 3D Shape Retrieval
We entered the sketch-based 3D shape retrieval track with a quick hack, a slightly modified version of the algorithm described in this paper. It is not designed for sketch based retrieval, so the result is expected.
Obviously, we have a lot to cover in this track.
(If we are to make excuses for our poor showing, our algorithm is designed
to accept polygon soup, point set, and other 3D models. Top contenders,
on the other hand, are tuned for oriented, manifold meshes, which allows
for additional feature extraction methods.)
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B. Li, A. Godil, T. Schreck, M. Alexa, T. Boubekeur, B. Bustos, J. Chen,
M. Eitz, T. Furuya, K. Hildebrand, S. Huang, H. Johan, A. Kuijper, R. Ohbuchi,
R. Richter, J. M. Saavedra, M. Sherer, T. Yanagimachi, G. J. Yoon, S. M.
Yoon, SHREC’12 Track: Sketch-Based 3D Shape Retrievall, to appear, Proc. Eurographics Workshop on 3D Object Retrieval, May , 2012. (PDF) |
Clustering to reduce training samples for manifold learning algorithms.
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Megumi Endoh, Tomohiro Yanagimachi, Ryutarou Ohbuchi, Efficient manifold
larning for 3D model retrieval by using clustering-based training sample
reduction, poster paper, Proc. IEEE Int'l Conf. on Acoustics, Speech, and
Signal Processing, Kyoto, Japan, March 2012 (2012). (PDF) |
2010年度
Distance metric learning via manifold learning for 3D model retrieval,
with a small trick.
Manifold learning algorithm by Zhou et al. may not work as intended for
some of high dimensional features, e.g. those produced by a large vocabulary
bag-of-features approach. We report an emprical technique to resolve that
issue in 3D model retrieval setting. We used the method for the Generic 3D Warehouse, Non-rigid shapes, and Range-scans tracks of the SHREC 2010 3D model retrieval contest.
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Ryutarou Ohbuchi, Takahiko Furuya, Distance Metric Learning and Feature
Combination for Shape-Based 3D Model Retrieval, poster paper, Proceedings of the ACM workshop on 3D object retrieval 2010, Firenze, Italy, (2010). (PDF) |
Dimension reduction of bag-of-visual features for 3D model retrieval improves
retrieval performance, while reducing the size of feature 1/10.
Our bag-of-visual features approach for 3D model retrieval produces features having high dimensionality, e.g., 30k dimensions. Reduction
in dimensionality is desired for faster comparison and compact storage.
We applied unsupervised and supervised dimension reduction algorithms,
and succedded in producing compact yet more salient feature vector. The
compressed feature is about 10 times the size of the original, while the
retrieval performance supassed that of the original.
For the non-rigid models of the McGill Shape Benchmark (MSB), supervised learning yielded near-perfect score of R-precision=99.8%. For the SHREC 2007 CAD model, BF-DSIFT achieved R-Precision=82% after surpervised learning. With a difficult and diverse SHREC 2006 benchmark, we achieved R-Precision=65.6% after semi-supervised learning.
我々が提案した3次元形状モデル検索の為の見かけに基づく特徴は，Bag-of-Featuresのヒストグラムとして特徴を求めます．その特徴は次元が高く（3万次元程度），またゼロ要素が多いものでした．特徴をコンパクトにしてその蓄積コストと検索の計算量を下げること，さらに，複数の意味カテゴリ（100個程度）を考慮した検索を実現すること，の2つを目的として，教師なし，および教師付き，の次元削減を試みました．その結果，検索性能が向上し，かつ，特徴量の次元を大幅に下げることに成功しました
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Ryutarou Ohbuchi, Masaki Tezuka, Takahiko Furuya, Takashi Oyobe, Squeezing
Bag-of-Features for Scalable and Semantic 3D Model Retrieval, Proc. 8th
International Workshop on Content-Based Multimedia Indexing (CBMI) 2010,
23-25 June 2010, Grenoble, France. (PDF) |
3次元形状の検索技術に関して「3次元画像コンファレンス」で招待講演します．
大渕 竜太郎，「形状の類似性による3次元モデルの検索」，3次元画像コンファレンス招待講演予稿．（2ページ）[Draft PDF]
3次元形状の検索技術に関する解説記事です．
大渕 竜太郎，「3次元形状の検索」，マルチメディア検索の最先端 第7回，映像情報メディア学会誌，Vol. 64, No. 7, (2010年7月号)
（6ページ）[Draft PDF]
We won some of the Shape REtrieval Contest (SHREC 2010) tracks （3次元モデル検索の国際コンテストにおいて，複数部門で1位～2位を獲得！）
We have entered this year's SHape REtrieval Contest (SHREC 2010), which compares retrieval performance and other features of 3D model retrieval
algorithms. This year, the contest included 8 tracks, such as 3D Generic 3D Warehouse, Non-rigid shapes, Range-scans, Large Scale Database, Protein Models, Part Correspondence, Architectural models, etc. We entered
the first four of the tracks listed above; Generic 3D Warehouse, Non-rigid shapes, Range-scans, and Large Scale Database,
We either tied for the 1st place or came in 2nd place in all the four tracks
we entered.
3次元モデル検索の国際コンテストSHREC（SHape REtrieval Contest）が今年も開催され，我々もGeneric 3D Warehouse, Non-rigid shapes, Range-scans, および Large Scale Databaseの4つの部門に参加しました．その4部門の全てで，1位または2位の好成績を収めました！
We came in the first place, with a large margin, among two entrants. We
used the method we published at ICCV S3D (PDF), a derivative of the BF-DSIFT, for the track, with a small modification. On top of the method described
in the ICCV S3D paper (PDF), we applied morphological filtering (e.g., dilation or closing) on rendered
range data in an attempt to reduce effects of cracks and gaps in the range
mesh. The morphological filter helped a bit in improving retrieval performance.
レンジスキャンによる検索の部門には2チームが参加し，我々が大差で1位です．（結果）
This track compares retrieval performance of deformable / articulated 3D
shapes. So a global shape descriptor geared for a rigid shape is not useful.
非剛体形状を検索する部門では，1位タイでした．（結果）全く異なる手法と1位を分けたのが面白いところです．
形状表現に対する汎用性という点では我々の手法が優位です．我々の手法は多様な形状表現（ポリゴンスープ，多様体メッシュ，点群，．．．）などに対応出来ます．しかし，同着1位のSmeetさんらの手法は，ほぼ，多様体メッシュに限定されます．
検索処理の手間・処理時間の点でもわれわれの手法は高速です．検索要求の提示から検索結果が戻るまで2～3秒です．
We tied for the first place among three entrants. The contest results was quite interesting, in that our completely (2D) appearance based method performed equally well with a method by Smeets et al that extracts feature on 2D manifold mesh embedded in 3D space.
Note that we used the same BF-DSIFT method combined with Manifold Ranking for the articulated shapes of this
non-rigid shapes track and for the rigid models of the Generic 3D Warehouse
track.
Our method is also computationally efficient. A query will be processed
in 2~3 seconds.
Z. Lian, A. Godil, T. Fabry, T. Furuya, J. Hermans, R. Ohbuchi, C. Shu,
D. Smeets, P. Suetens, D. Vandermeulen, S. Wuhrer. SHREC’10 Track: Non-rigid
3D Shape Retrieval. accepted, Eurographics Workshop on 3D Object Retrieval, 2010. [Draft pdf] [Results]
This track comapres retrieval performance of rigid 3D models crawled from
Google 3D Warehouse.
As noted above, we used the same BF-DSIFT method combined with Manifold Ranking for the articulated shapes of the
non-rigid shapes track above as well as for the rigid models of this Generic
3D Warehouse track.
The BF-DSIFT accepts a diverse set of shape representations, such as polygons soup,
manifold mesh, point set, etc., so far as they can be rendered as range
images. Yet the method is quite powerful in terms of retrieval performance.
It is also efficient, for it takes only a few second per query for a 1000model
database. (Other methods having comparable retrieval performance takes
much longer to process a query.)
This track compares retrieval performance of rigid 3D models using a larger,
10,000 model database. The 90% of the database, however, consists of junk
3D models, i.e.,"exploded" fragments of triangles filling an
entire bounding box. Actual "meaningful" 3D models are only about
10% of the 10,000 models.
We used, again, the same BF-DSIFT algorithm for this track, but without the Manifold Ranking this time.
The manifold ranking is based on global distribution of features that forms
"feature manifold", and the garbage 3D models affected its manifold.
Manifold ranking becomes computationally expensive as the number of features
to be considered increases.
2009年度
Shape-based Autotagging of 3D Models for Retrieval
（形状による自動タグ付けを用いたキーワードによる3Dモデル検索）
Specification of a query is one of the most fundamental issues in retrieving
multimedia objects such as images and 3D geometric models. While query
by 3D model example (or 2D sketch example) of a desired shape is quite
effective, text based search and retrieval of 3D models, or Query By Text
(QBT) approach, has its set of advantages. For example, specifying semantics
or intention may be easier than by query by shape example. An issue in
the QBT approach is the lack of labeled 3D models. This paper discusses
a method to add text tags to 3D models without tags. Given a set of 3D
models with tags, that is, the labeled training set, the method propagate
the likelihoods, or Tag Relevance Ranks, of the tags by means of multiple
overlapped manifold ranking [Zhou03] to the other models.
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Ryutarou Ohbuchi and Shun Kawamura, Shape-Based Autotagging of 3D Models
for Retrieval, 4th International Conference on Semantic and Digital Media
Technologies (SAMT 2009), Graz, Austria, Dec. 2-4, 2009. Lecture Notes in Computer Science, Volume5887/2009,
Springer (PDF) |
Scale-Weighted Dense Bag of Visual Features for 3D Model Retrieval from a Partial View 3D Model
（スケール重み付けを用いた密サンプル視覚特徴集合を用いた部分視点深さデータからの3次元モデル検索）
This paper describes a method for searching full 3D models from a range scanned 3D mesh from a viewpoint. The method is bases on the 3D model retrieval algorithm based on local visual features we have publised previously (Ohbuchi_SMI08). However, significant changes are made to deal with issues associated
with range scanning, such as cracks and jagged edges of the query 3D mesh.
Those changes include dense and random placement of Lowe's Scale Invariant
Feature Transform (SIFT) sample points, as well as an importance sampling
of lower-frequency, i.e., larger-scale, images of the Gaussian image pyramid
used in the SIFT. Experimental evaluation showed that the method significantly
ourperformed the methods in SHREC 2009 Partial View Retrieval track (organized
by Afzal Godil, et al.); the proposed method scored at Mean First Tier
= 37%, whereas the two entries to the SHREC scored 15% less, at Mean First
Tier = 22%.
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Ryutarou Ohbuchi and Takahiko Furuya, Scale-Weighted Dense Bag of Visual
Features for 3D Model Retrieval from a Partial View 3D Model, Proc. IEEE ICCV 2009 workshop on Search in 3D and Video (S3DV) 2009, Sept. 27, Kyoto, Japan. (PDF) |
Dense Sampling and Fast Encoding for 3D Model Retrieval Using Bag-of-Visual
Features
（密なサンプリングと高速な符号化を適用したバグオブフィーチャーズ法による3次元モデルの検索）
We have improved execution speed and retrieval performances of the 3D model
retrieval algorithm based on local visual features we have publised previously
(Ohbuchi_SMI08). The method employs dense random sampling of SIFT features in the multi-view
images to capture more features than the original, salient-point based
method. To compensate for the increased cost of computation due to much
larger number of local visual features, we adopted both GPU based feature
extraction [Ohbuchi_S-3D_08] and a fast randomized decision tree algorithm for codebook learning and
visual word encoding.
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Takahiko Furuya, RyutarouOhbuchi, Dense Sampling and Fast Encoding for
3D Model Retrieval Using Bag-of-Visual Features, Proc. ACM International Conference on Image and Video Retrieval 2009 (CIVR 2009), July 8-10, 2009, Santorini, Greece, (2009) (PDF) |
2008年度
Accelerating Bag-of-Features SIFT Algorithm for 3D Model Retrieval
（SIFT特徴とバグオブフィーチャーズを用いた3Dモデル検索の高速化）
We accelerated some of the steps for the Bag-of-Features SIFT algorithm
for 3D model retrieval (Ohbuchi_SMI08) we have published at the SMI 2008. We adopted Wu's algorithm for GPU-based SIFT feature extraction and table lookup for distance computation for a significant speedup without degradation in retrieval performance.
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RyutarouOhbuchi, Takahiko Furuya, Accelerating Bag-of-Features SIFT Algorithm
for 3D Model Retrieval, Proceedings of the SAMT 2008 Workshop on Semantic 3D Media, Koblenz, Germany, 2008, Dec. 3, 2008, pp. 23-30, (2008) (PDF) |
Ranking on Semantic Manifold for Semantic 3D Model Retrieval
（意味多様体上でのランキングを用いた意味的な3次元モデル検索）
Shape-based comparisons of 3D models is affected by shapes as well as semantics
of the 3D models. The semantics may be classified into long-term, well-established,
share semantics and per-query-session semantics (or intention). Previously,
most of so-called "semantic" retrieval algorithms took only one
of these types of semantics into consideration. Our novel approach combines
a set of long-term, established semantic classes with per-query-session
semantics for 3D model retrieval. Our method first learns, off-line, a
set of multiple semantic classes by using our semi-supervised dimension
reduction approach Ohbuchi, MIR07] to produce a "semantic manifold" from the input, ambient features.
The method then applies manifold ranking-based relevance feedback (RF)
on the semantic manifold. Our experimental evaluation showed that the RF
using manifold ranking performed on the semantic manifold far outpeforms
the same applied to the original ambient feature space.
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Ryutarou Ohbuchi, Toshiya Shimizu, Ranking on Semantic Manifold for Semantic
3D Model Retrieval, an oral paper, ACM MIR 2008, Vancouver, Canada, Oct. 2008. (PDF) (Acceptance ratio, including poster papers, is 21%) |
We won the SHape REtrieval Contest (SHREC) 2008 CAD and Generic Models
tracks
（SHREC2008のCADモデルおよび一般・多様モデル部門で優勝！）
We won the two tracks in the SHape REtrieval Contest (SHREC) 2008, the CAD Model Track 2008 and Generic Models Track. We entered the two tracks using our three algorithms.
We won the CAD Model Track 2 consecutive years.
U_Yama_A won with a very large margin as the graph shows. For the details, see the SHREC 2008 CAD track page.
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U_Yama_A  Akihiro Yamamoto, Masaki Tezuka, Toshiya Shimizu, Ryutarou Ohbuchi, SHREC'08 Entry: Semi-Supervised Learning for Semantic 3D Model Retrieval, pp 241-243, Proc. IEEE Shape Modeling International (SMI) 2008, June 4-6, Stony Brook, NY, USA (2008) (PDF).
U_Yama_B* Kunio Osada, Takahiko Furuya, Ryutarou Ohbuchi, SHREC'08 Entry: Local 2D Visual Features for CAD Model Retrieval, pp 237-238, Proc. IEEE Shape Modeling International (SMI) 2008, June 4-6, Stony Brook, NY, USA (2008) (PDF).
* Two methods named "U_Yama_B" that appear in the Generic models
track and the CAD Model Track are different. The U_Yama_A methods in the
two tracks, on the other hand, are the the same. |
Generic Models Track （多様・一般モデル検索部門）
U_Yama_A (the same method as the U_Yama_A) won the query set 1.
U_Yama_B (different method from the U_Yama_B for the CAD model track) won
the query set 2.
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U_Yama_A Akihiro Yamamoto, Masaki Tezuka, Toshiya Shimizu, Ryutarou Ohbuchi, SHREC'08
Entry: Semi-Supervised Learning for Semantic 3D Model Retrieval, pp 241-243,
Proc. IEEE Shape Modeling International (SMI) 2008, June 4-6, Stony Brook, NY, USA (2008) (PDF).
U_Yama_B* Kunio Osada, Takahiko Furuya, Ryutarou Ohbuchi, SHREC'08 Entry: Local Volumetric Features for 3D Model Retrieval, pp 245-246, Proc. IEEE Shape Modeling International (SMI) 2008, June 4-6, Stony Brook, NY, USA (2008) (PDF).
* Two methods named "U_Yama_B" that appear in the Generic models
track and the CAD Model Track are different. The U_Yama_A methods in the
two tracks, on the other hand, are the the same. |
Salient local visual features for shape-based 3D model retrieval
（顕著点における局所特徴を用いた3Dモデル形状類似検索）
We have developed a method to compare 3D models by their shape using a
set of local visual features. Our paper has been accepted as a regular
paper for the SMI 2008. By using local visual features, the method performs very well in comparing
shapes of articulated or deformable shapes, such as human beings, snakes,
insects, etc. Evaluated by using the McGill 3D Shape Benchmark for the articulated figures, it far outperformedall the global shape features
we tested.
For the rigid shapes, evaluated by using the Princeton Shape Benchmark, the method performs about as well as some of the most powerful method
employing global shape features.
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Ryutarou Ohbuchi, Kunio Osada, Takahiko Furuya, Tomohisa Banno, Salient
local visual featuers for shape-based 3D model retrieval, pp 93-102, Proc. IEEE Shape Modeling International (SMI) 2008, June 4-6, Stony Brook, NY, USA.(PDF) |
2007年度
Off-line supervised learning of semantic categories for semantic 3D model
retrieval
（意味カテゴリのオフライン教師付き学習を用いた3次元モデルの意味的な検索）
Similarities among 3D mordel "shapes" are often influenced by
their semantics as much as their shape. Semantic influcence may be captured
by on-line learning via relevance feedback, or by off-line learning of
semantic categories in a training database.
In this study, we have used a semi-supervised off-line learning of semantic
categories to boost 3D model retrieval performance. The method and experimental
evaluation results are described in the paper below. It is a draft of a
paper accepted as an oral paper for the 9th ACM SIGMM International Workshop on Multimedia Information Retrieval
(ACM MIR 2007) to be held in Augsburg, Germany. This research a part of Akihiro Yamamoto's
Master's thesis work.
半教師付き学習をつかって意味カテゴリを学習することにより，3次元モデルデータベースの検索性能を上げることに成功しました．この手法は，9th ACM SIGMM International Workshop on Multimedia Information Retrieval
(ACM MIR 2007)の口頭発表として採録された論文 (PDF)に述べてあります．
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Ryutarou Ohbuchi, Akihiro Yamamoto, Jun Kobayashi, Learning semantic categories
for 3D Model Retrieval, accepted, Proc. ACM MIR 2007, Augsburg, Germany, pp. 31-40, Oct. 2007. (PDF). |
Performance of this 3D model retrieval method was measured using SHREC
2006, and is listed here.
The new method outperforms our previous method by about 15% in terms of Mean First Tier (Highly relevant) measure (57.9%
for the new method), or the Mean Average Precision measure (0.618 for the
new method).
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Our new 3D model retrieval method (Method_A in the list) that employes off-line supervised learning of semantic categories is
evaluated by using SHREC 2006 benchmark.It outperforms our previous method
(based on unsupervised learning) as well as all the methods that entered
the SHREC 2006 by significant margin. |
We are the No.1 in the SHREC 2007 CAD model track
3次元CADモデル検索コンテスト（SHREC2007 CADトラック）で我々が優勝！
We won the SHREC 2007 (Shape Retrieval Contest) CAD model track. Our method won the competition by a significant margin, as the result shows.
The method we have employed is described in this short paper for the SHREC 2007 report. The contest's results are presented at a speciall
session at the Shape Modeling International 2007 (SMI 2007,) in Lyon, France.
We basically used the feature dimension reduction based on unsupervised learning described in the papers (ACM MIR 2006, R. Ohbuchi, J. Kobayashi, PDF) and (AMR 2007, R. Ohbuchi, J. Kobayashi, A. Yamamoto, PDF).
We also used the multiresolution shape comparison approach we have proviously
proposed (R. Ohbuchi, T. Takei, PG 2003, PDF).
It should be noted that we did not use "CAD models" for the learning;
we used 5,000 "generic" 3D models to train the Locally Linear
Embedding algorithm by Roweis et al.
SHREC 2007 ３Dモデル検索コンテストの CADモデル分野で1位になりました～！順位はここを見てください．
手法はこの論文に書かれています．教師無し学習を用いて多数の3次元モデルの特徴から，これら特徴の張る部分空間を学習し，その部分空間の測地線距離を用いる方法です．基本的なアプローチについてはこのMIR2006の論文に書かれています．
SHREC2007の構成とその結果について，詳しくは，
http://www.aimatshape.net/event/SHREC/UU-CS-2007-015.pdf
を参照してください．
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Jun Kobayashi, Akihiro Yamamoto, Toshiya Shimizu, Ryutarou Ohbuchi, A database-adaptive
distance measure for 3D model retrieval, SHREC 2007 CAD track paper, SMI 2007, June 2007 (PDF) |
Comparison of dimension reduction method for 3D model retrieval
（3Dモデル検索のための次元削減手法の比較）
Our paper that comapred the dimension reduction methods have been presented
at the AMR 2007 workshop in Paris, France in July.
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Ryutarou Ohbuchi, Jun Kobayashi, Akihiro Yamamoto, and Toshiya Shimizu,
Comparison of dimension reduction method for database-adaptive 3D model
retrieval, In Proc. Fifth International Workshop on Adaptive Multimedia
Retrieval (AMR 2007), Paris, France, July 2007.In Proceedings of the Fifth International Workshop on Adaptive Multimedia
Retrieval (AMR 2007), Paris, France, July 2007.(PDF) |
Tatsuma Atsushi's 3D model retrieval page
Our friend Tatsuma Atsushi has several powerful 3D model retrieval methodls.
It appears that one of his methods would have tied No.1 in the SHREC 2007 Face retrieval contest if his method were to enter the track.
The method achieved NDCG@25 of 0.6379944 using the SHREC 2006 benchmark.
This is quite remarkable.
He also has a nifty online 3D model retrieval demo "3Doodle"
.
My Background
I earned my Ph.D degree at
Computer Science Department at the UNC-Chapel Hill where
visionaries in computer science teach, and have graduated. Those who teach
there include Prof. Frederick. P. Brooks, Prof. Stephen Pizer, Prof. Henry
Fuchs, Prof. Turner Whitted, and Prof. Dinesh Manocha. My principal advisor,
Prof.
Henry Fuchs, is a wonderfully
stimulating person to be with. Those who graduated frequent authors lists
at the prestigious SIGGRAPH. Chapel Hill is where I switched form computer
architecture to computer graphics and worked on volume visualization and
augmented reality for medical applications. There is something new andinteresting
happening at UNC-Chapel Hill in terms of computer graphics. So please check
it out.
From January 1994 to March 1999, I worked for
IBM
Tokyo Research Laboratory. There, I worked in Advanced
Graphics and CAD group. I enjoyed working there, with Masaki Aono,
Hiroshi
Masuda, Kazunori Miyata, Takayuki
Ito, Kenji Shimada, Atushi Yamada, and others.
I worked on virtual environment surgical
simulation system for National Cancer Center
in Tsukiji, Tokyo, realistic image synthesis
by using quasi-random sequence, digital watermarking
of 3D models, compression techniques for
CAD models, and others. .
Contact
Ryutarou Ohbuchi （Japanese -> 大渕 竜太郎）
Computer Science Department
University of Yamanashi
4-3-11 Takeda, Kofu-shi,
Yamanashi-ken, 400-8511
Japan
Phone: +81-55-220-8570
my_last_name AT yamanashi DOT ac DOT jp
