Graduate School of Engineering, University of Yamanashi, Yamanashi, Japan
Computer Science and Engineering Department
University of Yamanashi
4-3-11 Takeda, Kofu-shi, Yamanashi-ken
ohbuchi *a t* yamanashi d0t ac d0t jp
Computer Science Department, University of Yamanashi
I teach at Computer Science and Engineering Department (コンピュータ理工学科) of the faculty of
engineering, University of Yamanashi, located in Kofu-city, Yamanashi Prefecture, Japan.
My publication list and resume
Find me in , or ArnetMinar
2014年度 前期 Spring semester 2014
計算機アーキテクチャI I 2014 (Computer Architecture II)
組込みシステム2014 (Embedded Systems)
情報と職業 2014 (Information Technology and Occupation 2014)
組み込みシステム概論 2014 （大渕担当分）(Intro. to Embedded Systems (Graduate Course, omnibus))
2014年度 後期 Fall semester 2014
科学技術英語演習 2014 (Scientific and Technical English Practices)
意味的マルチメディア処理特論 2014 (Semantic Multimedia Processing (Graduate Course))
修士論文の作り方 （お茶の水女子大学 伊藤先生作成）
News and updates
2015年度 (fiscal 2015)
Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D
Aggregation of loca features has a large impact on accuracy and cost of
object recognition in or retrieval of images or 3D shape models. Classic
bag-of-words or bag-of-features approach has been followed up by such methods
as Vector of Locally Aggregarted Descriptor (VLAD), Fisher Vector (FV)
coding, Super Vector (SV) coding, or Locality-constrained Linear Coding
(LLC). We propose a new feature aggregation method called Diffusion-on-Manifold
(DM) that exploits structure of potentially non-linear manifold of local
Our experimental evaluation using 3D model retrieval setting shows that
DM often outperforms previous feature aggregation methods succh as FV,
SV, LLC, or VLAD.
We also propose a new local 3D geometrical feature called Position and
Orientation Distribution (POD) used in the evaluation experiment.
An Unsupervised Approach for Comparing Styles of Illustrations
In this paper, we propose an unsupervised approach to achieve accurate
and efficient stylistic comparison among illustrations. The proposed algorithm
combines heterogeneous local visual features extracted densely. These features
are aggregated into a feature vector per illustration prior to be treated
with distance metric learning based on unsupervised dimension reduction
for saliency and compactness.
|| Takahiko Furuya, Shigeru Kuriyama, and Ryutarou Ohbuchi, An Unsupervised
Approach for Comparing Styles of Illustrationsl, oral paper, Proc. 13th International Workshopn on Content-Based Multimedia Indexing (CBMI) 2015, Prague, Czech Republic, June 10-12, 2015, (PDF).
Scalable Part-Based 3D Model Retrieval by using Randomized Sub-Volume Partitioning
This paper presents a scalable algorithm for part-based 3D model retrieval.
Given a part based query, e.g., a 3D model of a jet engine, the system
searches through a 3D model database, and retrieval 3D models that contains,
as their subparts, shape(s) similar to the jet engine. The; algorithm employs
RSVP, or Randomized Sub-Volume Partitioning, algorithm accelerated by using
late-binding local feature aggregation. To accelerate the search through
the large number of subvolumes (e.g., 1350 subvolumes per 3D model, and
50k 3D models per database), the algorithmcombines feature dimensionality
reduction with hashing into compact binary codes.
|| Takahiko Furuya, Seiya Kurabe, and Ryutarou Ohbuchi, Randomized Sub-Volume
Partitioning for Part-Based 3D Model Retrieval, oral paper, Proc. Eurographics Workshop on 3D Object Retrieval (EG 3DOR) 2015, Zurich, Switzerland, May 2-3, 2015, DOI:10.2312/3dor.20151050 (PDF).
3D SHape REtrieval Contest (SHREC) 2015 results
There are 9 tracks for this year's 3D SHape REtrieval Contest (SHREC) 2015. We participated in the following two tracks, and finished 1st in both
of the tracks!
Non-rigid 3D Shape Retrieval: 1st place
The task of the Non-rigid 3D Shape Retrieval track is to retrieve highly articulated and/or deformable 3D shape. The competition
was tight, with many team achieving high accuracy as the evaluation results show. In the ende, of 11 teams participated, we finished 1st.
We combined our LSF 3D local statistical shape feature, aggregated by using Super Vector Coding,
with (single-domain) unsupervised similarity metric learning by using Manifold
Ranking by Zhou, et al.
Range-Scans based 3D Shape Retrieval: 1st place
The task of the Range-Scans based 3D Shape Retrieval track is to retrieve (complete) 3D shape based on a single view range-scan data.
Six teams participated in this track. As the evaluation results show, we finished 1st in the track with quite a large margin.
We tried to exploit shape similarities among (full) 3D models as well as
similarity between a single-view range-scan data (the query) and (full)
3D models in the database. To do so, we employed our Cross-Domain Manifold Ranking (CDMR) similarity metric learning algorithm. To compare a range-scan query with
(full) 3D models, we used our view-based algorithm updated with the Super Vector Coding. To compare among (full) 3D models,
we used our 3D Visual Feature Fusion (3DVFF) algorithm that effectively fuses multiple visual features.
Lightweight binary voxel shape features for 3D data matching and retrieval
In this paper, we propose light weight features for voxel-based 3D shape
definitions. The features, called 3DBRIEF and 3DORB, are inspired by simple
light-weight 2D local image features BRIEF bay Calonder, et al and ORB
by Rublee, et al. These features produces binary bitstring as their feature
vector. They have small lower computational costs and lower memory footprints.
We extend the 3DORB, which is the 3D version of ORB, for improved retrieval
accuracy albeit higher computational cost. We evaluate the features in
shape similarity based 3D model retrieval setting by using Bag-of-Features
framework in Hamming space to aggregate a set of 3DORB or 3DBRIEF features
extracted from a 3D model.
|| Takahiro Matsuda, Takahiko Furuya, Ryutarou Ohbuchi, Lightweight binary
voxel shape features for 3D data matching and retrieval, Oral paper, Proc. First IEEE Int’l Conf. on Multimedia Big Data (BigMM) 2015, 20-22
April 2015, Beijing, China. (PDF)
2014年度 (fiscal 2014)
Hashing Cross-Modal Manifold for Scalable Sketh-based 3D Model Retrieval
Similarity metric learning performed across sketch and 3D model domains described in our previous paper (Springer MTAP journal) improves retrieval accuracy of sketch-based 3D model retrieval. However,
it was rather slow for a 3D model database of substantial size, e.g., containing
100k or more 3D models.
In this paper, we propose an alrogithm to speed up the retrieval phase
of the cross-domain similarity metric learning algorithm by a combination
of feature dimensionality reduction and hashing into Hamming (binary feature
vector) space. Similarity computation between a pair of few hundred bit
binary vectors in Hamming space is very fast so that a database having
100k or more 3D models can be searched in about a second. Also, small size
of the binary vectors prompts on-main-memory or on-GPU-memory processing.
The proposed algorithm also employs a set of improved view-based features
based on the work reported in the BMVC 2014 paper. As a result, our proposed method is both accurate and efficient.
A comparison of 3D shape retrieval methods based on a large-scale benchmark
supporting multimodal queries
This article compares varisou methods for 3D model retrieval by using multimodal
queries, e.g., by 3D model and by sketch.
|| Bo Li, Yijuan Lu, Chunyuan Li, Afzal Godil, Tobias Schreck, Masaki Aono,
Martin Burtscher, Qiang Chen, Nihad Karim Chowdhury, Bin Fang, Hongbo Fu,
Takahiko Furuya, Haisheng Li, Jianzhuang Liu, Henry Johan, Ryuichi Kosaka,
Hitoshi Koyanagi, Ryutarou Ohbuchi, Atsushi Tatsuma, Yajuan Wan, Chaoli
Zhang, Changqing Zou, A comparison of 3D shape retrieval methods based
on a large-scale benchmark supporting multimodal queries, Computer Vision
and Image Understanding, doi:10.1016/j.cviu.2014.10.006
Fusing Multiple Features for Shape-based 3D Model Retrieval
This work brings proposes Multi-Feature Manifold Ranking (MFAMR) algorithm
for a joint distance metric learning among multiple feature vectors in
high dimensional space. MFAMR, a variation of manifold-based distance metric
learning algorithm, produces better overall distance (or similarity) among
objects, each of which are described by more than one features, than simple
linear combination of distances due to the features. For efficiency, anchoring
is employed to reduce number of feature points to form the manifold.
The paper also updates our BF-DSIFT (ca. 2009) algorithm to the year 2014,
by employing Super Vector coding to aggregate local features (SIFT features)
per 3D model, resulting in SV-DSIFT feature. In addition, the paper proposes
LL-MO1SIFT for rigid object comparison by using Locally Linear Coding (LLC).
The combination of the MFAMR and two updated features, SV-DSIFT and LL-MO1SIFT,
significantly improves accuracy of 3D-model to 3D-model comparison.
|| Takahiko FURUYA and Ryutarou OHBUCHI, Fusing Multiple Features for Shape-based
3D Model Retrieval, Oral paper, Proceedings of British Machine Vision Conference (BMVC) 2014, Nottingham, U.K., September 1-September 5, 2014. (PDF) (Acceptance rate is 7.7% (33/431) for oral papers and 30% (131/431) for combined poster and oral papers.)
Similarity Metric Learning on Cross-Domain Manifold for Sketch-based 3D
Takahiko Furuya applied cross-domain similarity metric learning to improve
accuracy of sketch-based 3D model retrieval. It relates sketches and 3D
models, which are in different feature domains, by using sketch-to-sketch
similarity, sketch-to-3D model similarity, and 3D model-to-3D model similarity.
In addition, if available, class labels may be recruited for semi-supervised
|| Takahiko FURUYA and Ryutarou OHBUCHI, Similarity Metric Learning for Sketch-based
3D Object Retrieval, Multimedia Tools and Applications (MTAP), Springer,
DOI: 10.1007/s11042-014-2171-3 (published online, July 2014)
画像電子学会誌 （2013～2014年度） 最優秀論文賞を受賞！！
古屋 貴彦さんと大渕 竜太郎の執筆した以下の論文が，画像電子学会誌に2013～2014年度の2年間に掲載された全論文の中から2編以内の論文の著者らに贈られる「最優秀論文賞」を受賞しました．（2014年6月29日の受賞式の写真を追加しました．2014年7月1日更新）
Following paper, co-authored by Takahiko Furuya and myself (Ryutarou Ohbuchi)
received "Best paper"award from the Institute of Image and Electronics Engineers of Japan (IIEEJ).
It is a biennial award given to two papers selected from all the papers
published in the Journal of IIEEJ during the past two years, i.e., 2012
and 2013. (Updated: June 29, 2014 with the picture of award ceremony..)
Award ceremony on June 29, 2014.
| 古屋貴彦，大渕竜太郎, 見かけ特徴の組み合わせと距離尺度の学習を用いた3次元形状類似検索, 画像電子学会誌, 第42巻，4号, pp.438-447,
2013年8月. （2012-1013年度 画像電子学会 最優秀論文賞を受賞）
Takahiko FURUYA，Ryutarou OHBUCHI, Visual Feature Combination and Distance
Metric Learning for 3D Shape Retrieval, The journal of the Institute of
Image Electronics Engineers of japan (Journal of the IIEEJ), Vol.42, No.4,
pp.438-447, August 2013. (PDF in Japanese)
(http://www.iieej.org/gakkaishi3/IIEEJ_Vol42-No4ori.pdf) (Recieved "Best paper" award from Institute of Image Electronics Engineers of Japan (IIEEJ),
which was awarded to two papers published during years 2012 and 2013. (A
BF-DSIFT paper is one of the "Most cited papers beofore the era of ICMR" for CIVR 2009, according to ACM SIGMM records
古屋 貴彦さんと大渕がACM International Conｆerence on Image and Video Retrieval （CIVR)
2009 で発表した以下の論文が，ACM SIGMM Records, Volume 6, Issue 1, March, 2014 の記事の中で「ICMRコンファレンス以前(のマルチメディア検索関連の国際学会）に最も参照された論文」の一つに選ばれました．具体的には，Google Scholarによる非参照数が，ACM CIVR 2009で発表された論文中4位（2014年2月17-18日現在で57件）だったそうです．（現在のACM Digital Libraryによる被参照数とGoogle Scholarによるひ参照数．） また，この論文の元になった，Osadaさんらの"Salient local visual feautres for shape-based 3D model retrieval" （IEEE SMI 2008 ）も多くの論文に参照されています．
The paper below by Takahiko Furuya and Ryutarou Ohbuchi that appeared in
Proceedings of the CIVR 2009 has been recognized as one of "Most cited papers before the era of ICMR" in an article by Dr. Erwin M. Bakker that appeared in ACM SIGMM Records, Volume 6, Issue 1, March, 2014 (ISSN 1947-4958). It is the 4th most cited paper among the papers accepted
for CIVR 2009. It received 57 Google Scholar citations as of February 17-18,
2014, according to the article. Tha paper is a part of Takahiko Furuya's
master's thesis research. (Here are current citation counts of this paper
according to ACM Digital Library and Google Scholar.)
According to Google Scholar Metrics, as of 2015/04/15, ACM CIVR ranks 17th among the subcategory "Multimedia" of publication venues. The ranking includes both journals and conference proceedings. Of all the papers published at the ACM CIVR conferences, our paper published at CIVR 2009 is ranked 4th in terms of h5-index, again as of 2015/04/15.
The paper describes Bag-of-Features Dense SIFT (BF-DSIFT) algorithm for shape-based 3D model to 3D model comparison.
|| 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) (doi>10.1145/1646396.1646430) (Recognized as one of "Most cited papers before the era of ICMR" in ACM SIGMM Records, Volume 6, Issue 1, March 2014 (ISSN 1947-4598).
SHREC 2014: Extended Large Scale Sketch-Based 3D Shape Retrieval (We placed 2nd.)
This track of the SHape REtreival Contest 2014 (SHREC 2014) compared retrieval accuracy of sketch-based 3D model retrieval algorithms.
Our algorithm by Takahiko Furuya placed 2nd. The first place went (again!) to our friends, Tatsuma and Aono's team at Toyohashi University of Technology. The algorithm by Tatsuma
et al combined a powerful visual feature with a clever unsupervised distance
metric learning algorithm. Tatsuma's method won by a large margine.
Conglaturations (again!) to Prof. Aono and Prof. Tatsuma! (But we'll try
to win the next time.)
|| B. Li, Y. Lu, C. Li, A. Godil, T. Schreck, M. Aono, M. Burtscher, H. Fu,
T. Furuya, H. Johan, J. Liu, R. Ohbuchi, A. Tatsuma, and C. Zou , A. Extended
Large Scale Sketch-Based 3D Shape Retrieval, Proc. Eurographics Workshop
on 3D Object Retrieval 2014 (3DOR 2014), pp. 121-130, 2014. (DOI: 10.2312/3dor.20141058)
(PDF) (full paper review)
SHREC 2014: Large Scale Comprehensive 3D Shape Retrieval (We placed 2nd.)
This track of the SHape REtreival Contest 2014 (SHREC 2014) compared retrieval accuracy of 3D model retrieval algorithms that uses
3D model examples as queries.. Our algorithm by Takahiko Furuya placed
2nd. The first place went to our friends, Tatsuma and Aono's team at Toyohashi University of Technology, Japan. They also ranked
first in the sketch-based retrieval track above. Conglaturations to Prof.
Aono and Prof. Tatsuma! (But we'll try to win the next time.)
Prof. Aono is a good friend of Ohbuchi's; we are ex-colleagues at IBM
|| B. Li, Y. Lu, C. Li, A. Godil, T. Schreck, M. Aono, Q. Chen, N. K. Chowdhury,
B. Fang, T. Furuya, H. Johan, R. Kosaka, H. Koyanagi, R. Ohbuchi, A. Tatsuma,
Large Scale Comprehensive 3D Shape Retrieval, Proc. Eurographics Workshop
on 3D Object Retrieval 2014 (3DOR 2014), pp. 131-140, 2014. (DOI: 10.2312/3dor.20141059)
(PDF) (full paper review)
2013年度 (fiscal 2013)
A comparison of methods for sketch-based 3D shape retrieval
Sketch-based 3D shape retrieval has become an important research topic
in content-based 3D object retrieval. To foster this research area, two
Shape Retrieval Contest (SHREC) tracks on this topic have been organized
by us in 2012 and 2013 based on a small-scale and large-scale benchmarks,
respectively. Six and five (nine in total) distinct sketch-based 3D shape
retrieval methods have competed each other in these two contests, respectively.
To measure and compare the performance of the top participating and other
existing promising sketch-based 3D shape retrieval methods and solicit
the state-of-the-art approaches, we perform a more comprehensive comparison
of fifteen best (four top participating algorithms and eleven additional
state-of-the-art methods) retrieval methods by completing the evaluation
of each method on both benchmarks. The benchmarks, results, and evaluation
tools for the two tracks are publicly available on our websites [1,2].
|| Bo Li, Yijuan Lu, Afzal Godil, Tobias Schreck, Benjamin Bustos, Alfredo
Ferreira, Takahiko Furuya, Manuel J. Fonseca, Henry Johan, Takahiro Matsuda,
Ryutarou Ohbuchi, Pedro B. Pascoal, Jose M. Saavedra, A comparison of methods
for sketch-based 3D shape retrieval, Computer Vision and Image Understanding (CVIU), Volume 119, February 2014, Pages 57–80, ISSN 1077-3142, (PDF) http://dx.doi.org/10.1016/j.cviu.2013.11.008
Visual Saliency Weighting and Cross-Domain Manifold Ranking for Sketch-based
Retrieval of images (photos) presented with a line drawing sketch is not
an easy task. Sketches vary from person to person, with wobbling lines,
disconnected lines, different level of abstraction, different style, etc.
In addition, person often sketch only an object of interest only, totally
ignoreing background and accompanying (yet uninterested) objects. These
background and uninterested objects in images get in the way of accurate
comparison and retrieval. In this paper, we combine saliency detection
algorithm with a distance metric learning algorithm using Cross-Domain
Manifold Ranking for more accurate and effective sketch based image retrieval.
|| Takahiko Furuya, Ryutarou Ohbuchi, Visual Saliency Weighting and Cross-Domain
Manifold Ranking for Sketch-based Image Retrieval, regular paper, Proc. Multi-Media Modeling (MMM) 2014, January, 2014, Dublin, Ireland. (PDF) Springer LNCS Volume 8325, 2014, pp 37-49, 2014.(http://link.springer.com/chapter/10.1007%2F978-3-319-04114-8_4)
NICOGRAPH 2013 で古屋 貴彦，松田 隆広，栗田 侑希紀，大渕 竜太郎の4名が，優れた論文に対して贈られるNICOGRAPH 2013 優秀論文賞を受賞しました！
Following conference paper, co-authored by Takahiko Furuya, Takahiro Matsuda,
Yukinori Kurita, and Ryutarou Ohbuchi, has received "Best paper award" at NICOGRAPH 2013 conference held in Katsunuma, Japan from Nov.8~9, 2014.
|| 古屋 貴彦, 松田 隆広, 栗田 侑希紀, 大渕 竜太郎, 多視点画像特徴の多様体を用いたスケッチによる3Dモデルの検索，NICOGRAPH
2013, 2013年11月8~9日. (NICOGRAPH 2013 優秀論文賞受賞) (PDF)
Ranking on Cross-Domain Manifold for Sketch-based 3D Model Retrieval
Sketch-based 3D model retrieval algorithms compare a query, a line drawing
sketch, and 3D models for similarity by rendering the 3D models into line
drawing-like images. Still, retrieval accuracies of previous algorithms
remained low, as sets of features, one of sketches and the other of rendered
images of 3D models, are quite different; they are said to lie in different
domains. This paper proposes Cross-Domain Manifold Ranking (CDMR), an algorithm
that effectively compares two sets of features that lie in different domains.
Experimental evaluation by using sketch-based 3D model retrieval benchmarks
showed that the CDMR is more accurate than state-of-the-art sketch-based
3D model retrieval algorithms.
|| Takahiko Furuya, Ryutarou Ohbuchi, Ranking on Cross-Domain Manifold for
Sketch-based 3D Model Retrieval, regular paper, Proc. CyberWorlds 2013, pp. 274-281, Oct. 21-23, 2013, Tokyo, Japan. (PDF) (DOI:10.1109/CW.2013.60)
Talk slide Long (PDF)
CW 2013 talk slide (short) (PDF)
View-Clustering and Manifold Learning for Sketch-based 3D Model Retrieval
In this paper, we propose an algorithm that employs manifold-learning based
dimension reduction for sketch-based 3D model retrieval. The algorithm
compares multi-view rendering of 3D models with the 2D sketch. In order
to lower the cost of training a manifold learning algorithm, namely, the
LLE, we reduce number of training samples by clustering, either in feature
space or in view space. Experimental evaluation has shown that both view
space clustering and feature space clustering lowers training cost by more
than 10 times while significantly improving retrieval accuracy. A compact
50 dimensional feature after the dimension reduction is much faster to
compare, and its retrieval accuracy is 40% better than the original 30k
|| Yukinori Kurita, Ryutarou Ohbuchi, View-Clustering and Manifold Learning
for Sketch-based 3D Model Retrieval, short paper, Proc. CyberWorlds 2013, pp. 282-285, Oct. 21-23, 2013, Tokyo, Japan. (PDF) (DOI: 10.1109/CW.2013.70)
見かけ特徴の組み合わせと距離尺度の学習を用いた3次元形状類似検索 (Visual Feature Combination and Distance
Metric Learning for 3D Shape Retrieval)
画像電子学会誌 （2012～2013年度） 最優秀論文賞を受賞！！
古屋 貴彦さんと大渕 竜太郎の執筆した以下の論文が，画像電子学会誌に2012～2013年度の2年間に掲載された全論文の中から2編以内の論文の著者らに贈られる「最優秀論文賞」を受賞しました．（2014年5月30日更新）
This paper describes a shape-based 3D model retrieval algorithm that employs
multi-view rendering and densely-sampled local visual features, the BF-DSIFT,
to compare 3D models. To further boost retrieval accuracy, the algorithm
uses manifold-based distance metric learning and fursion of multiple features.
Experimental evaluation of retrieval accuracy is conducted by using multiple
|| 古屋貴彦，大渕竜太郎, 見かけ特徴の組み合わせと距離尺度の学習を用いた3次元形状類似検索, 画像電子学会誌, 第42巻，4号, pp.438-447,
2013年8月.（2012-1013年度 画像電子学会 最優秀論文賞を受賞）
Takahiko FURUYA，Ryutarou OHBUCHI, Visual Feature Combination and Distance
Metric Learning for 3D Shape Retrieval, The journal of the Institute of
Image Electronics Engineers of Japan (Journal of the IIEEJ), Vol.42, No.4,
pp.438-447, August 2013. (PDF in Japanese)
(http://www.iieej.org/gakkaishi3/IIEEJ_Vol42-No4ori.pdf) (Recipient of "Best paper" award from Institute of Image Electronics Engineers of Japan (IIEEJ),
which is awarted to two papers published during years 2012 and 2013. (A
DENSELY SAMPLED LOCAL VISUAL FEATURES ON 3D MESH FOR RETRIEVAL
Local Depth-SIFT (LD-SIFT) algorithm by Darom, et al.has shown good retrieval
accuracy for 3D models defined as densely sampled manifold mesh. However,
it has two shortcomings. The LD-SIFT requires the input mesh to be densely
and evenly sampled. Furthermore, the LD-SIFT can’t handle 3D models defined
as a set of multiple connected components or a polygon soup. This paper
proposes two extensions to the LD-SIFT to alleviate these weaknesses. First
extension shuns interest point detection, and employs dense sampling on
the mesh. Second extension employs remeshing by dense sample points followed
by interest point detection a la LD-SIFT. Experiments using three different
benchmark databases showed that the proposed algorithms significantly outperform
the LD-SIFT in terms of retrieval accuracy.
|| Yuya Ohishi, Ryutarou Ohbuchi, DENSELY SAMPLED LOCAL VISUAL FEATURES ON
3D MESH FOR RETRIEVAL, short paper, Proc. WIAMIS 2013, pp.1-4, July 3-5, 2013, Paris, France. (PDF) (DOI: 10.1109/WIAMIS.2013.6616166)
Visual ComputingグラフィクスとCAD合同シンポジウムにおける優れた発表に対し，古屋 貴彦さんが情報処理学会・グラフィクスとCAD研究会優秀研究発表賞を受賞しました．
Takahiko Furuya, lead author and presentor of the followng paper, received
"IPSJ SIG on Graphics and CAD outstanding research presentation" award at the Joint Visual Computing/Graphics & CAD Symposium 2013 held in
Aomori, Japan during June 22~June 23, 2013.
| 古屋 貴彦, 大渕 竜太郎, クロスドメイン多様体ランキングを用いたスケッチによる3Dモデルの検索，Proc. Visual ComputingグラフィクスとCAD合同シンポジウム
2013, 2013年6月22日~23日. (情報処理学会・グラフィクスとCAD研究会優秀研究発表賞受賞)
The picture to the left is of award ceremony held on June 28, 2014.
ICPR 2012 Tutorial AM-04 （ICPR 2012 でチュートリアル講義を行いました）
3D Shape Analysis and Retrieval - Recent Advances and Trends （3次元形状解析と検索
‐ 最近の動向 ‐）
Tutorial slides (at Google Sites)
Hamid Laga* and RyutarouOhbuchi** （講師：ハミド ラーガさんと大渕）
*School of Mathematics and Statistics, University of South Australia, Australia
**Computer Science and Engineering Department, University of Yamanashi,
- Introduction (PDF)
- Rigid 3D Shape Analysis (PDF)
- Non-rigid 3D Shape Analysis (PDF)
- Manifold-based Analysis of Deformable Surfaces (PDF)
- Learning (PDF)
- Querying 3D Shape Databases (PDF)
- Applications (PDF)
- Summary (PDF)
Local Geometry Adaptive Manifold Re-Ranking for Shape-Based 3D Object Retrieva
This paper proposes an improvement to Manifold Ranking algorithm used for
search results ranking in the context of shape-based 3D model retrieval.
Manifold Ranking algorithm by Zhou et al estimates, given a set of high-dimensional
feature vectors, a lower-dimensional manifold on which the features lie.
It then computes diffusion-based distances from a feature vector (or feature
vectors) to the other feature vectors on the manifold. When applied to
content-based retrieval, overall retrieval accuracy is significantly better
than a “simple” fixed distance metric. However, in a small neighborhood
of query, retrieval ranks obtained by a “simple” distance metric (e.g.,
L1-norm) performs better than those obtained by Manifold Ranking. Proposed
re-ranking algorithm tries to combine ranking results due to both simple
distance metric and Manifold Ranking in an automatic query expansion framework
for better ranking results. Experimental evaluation has shown that the
proposed method is effective in improving retrieval accuracy.
|| Ryutarou Ohbuchi, Yukinori Kurita, Local Geometry Adaptive Manifold Re-Ranking
for Shape-Based 3D Object Retrieval, in Proc. ACM Multimedia 2012 (ACM MM 2012), short paper, pp. 901-904, Oct. 29-Nov. 2, 2012, Nara, Japan. (PDF) (DOI: 10.1145/2393347.2396342)
A comparison of methods for non-rigid 3D shape retrieval
Non-rigid 3D shape retrieval has become an active and important research
topic in content-based 3D object retrieval. The aim of this paper is to
measure and compare the performance of state-of-the-art methods for
non-rigid 3D shape retrieval. The paper develops a new benchmark
consisting of 600 non-rigid 3D watertight meshes, which are equally
classified into 30 categories, to carry out experiments for 11 different
algorithms, whose retrieval accuracies are evaluated using six commonly
utilized measures. Models and evaluation tools of the new benchmark are
publicly available on our web site 
||Zhouhui Lian, Afzal Godil, Benjamin Bustos, Mohamed Daoudi, Jeroen Hermans,
Shun Kawamura, Yukinori Kurita, Guillaume Lavoué, Hien Van Nguyen, Ryutarou
Ohbuchi, Yuki Ohkita, Yuya Ohishi, Fatih Porikli, Martin Reuter, Ivan Sipiran,
Dirk Smeets, Paul Suetens, Hedi Tabia, Dirk Vandermeulen, A comparison
of methods for non-rigid 3D shape retrieval, Pattern Recognition, Volume 46, Issue 1, Elsevier, January, 2013, ISSN 0031-3203, DOI:10.1016/j.patcog.2012.07.014.,
URL: www.sciencedirect.com/science/article/pii/S0031320312003305 (PDF)
Non-rigid 3D Model Retrieval Using Set of Local Statistical Features
In this paper, we present a 3D model retrieval algorithm that employs a
bag of 3D local geometrical features.The local feature is a statistical
feature computed from oriented point set. It is inherently invariant to
translation, (uniform) scaling, and rotation. For non-rigid model retrieval,
the algorithm achieves good retrieval accuracy.
This paper is a condensed version of Yuki Ohkita's bachelor's thesis, who
graduated on March 2009.
|| Yuki Ohkita, Yuya Ohishi, Takahiko Furuya, Ryutarou Ohbuchi, Non-rigid
3D Model Retrieval Using Set of Local Statistical Features, Proc. IEEE ICME 2012 Workshop on Hot Topics in 3D Multimedia (Hot3D), July 9, 2012, Melbourne Australia, DOI:10.1109/ICMEW.2012.109 (PDF)
Supervised, geometry-aware segmentation of 3D mesh models
This work presents a supervised segmentation algorithm for 3D manifold
mesh models. User teaches the system a segment by painting, a small subset
of polygons of a mesh. After a few segments are specified, the system runs
semi-supervised learning algorithm guided by local surface geometrical
features to propagate labels over the 3D model manifold. We used an algorithm
that mimic diffusion-like process on the manifold surface, Manifold Ranking
by Zhou, et al. for the propagation.
This paper is a summary of Keisuke Banba's Master's thesis, who graduated
on March, 2011.
|| Keisuke Banba, Ryutarou Ohbuchi, Supervised, geometry-aware segmentation
of 3D mesh models, Proc. IEEE ICME 2012 Workshop on Human-Focused Communications in the 3D
Continuum, July 9, 2012, Melbourne Australia. DOI: 10.1109/ICMEW.2012.16 (PDF)
Local Geometrical Feature with Positional Context for Shape-based 3D Model
A set of local distance histograms computed on manifld 3D mesh surfaces
can be quite effective in comparing deformable or articulated models. However,
these features are often not discriminative enough. For example, these
features would give the same set of features even if the 3D model goes
through volume-changing deformations so far as the surface distances are
preserved. In this work, we tried to combine local geometrical feature
computed in local 3D Euclidian space with local distance histograms. We
also attempted to make the features computable on a multiply-connected
or non-manifold 3D models by remeshing the surfaces into singly connected
This work is a summary of a half of Shun Kawamura's master's thesis, who
graduated on March 2011. He also worked on Shape-Based Autotagging of 3D
|| Shun Kawamura, Kazuya Usui, Takahiko Furuya and Ryutarou Ohbuchi, Local
Geometrical Feature with Positional Context for Shape-based 3D Model Retrieval,
poster paper, Proc. Eurographics 2012 Workshop on 3D Object Retrieval, pp.55-58,DOI: 10.2312/3DOR/3DOR12/055-058, 2012.. (PDF)
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．
|| 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, Proc. Eurographics Workshop
on 3D Object Retrieval, May , 2012. (DOI: 10.2312/3DOR/3DOR12/119-126) (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.)
|| 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, Proc. Eurographics
Workshop on 3D Object Retrieval, May , 2012. (DOI: 10.2312/3DOR/3DOR12/109-118) (PDF)
Clustering to reduce training samples for manifold learning algorithms.
|| 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 2012 (IEEE ICASSP 2012), Kyoto, Japan, March 2012 (2012). (DOI: 10.1109/ICASSP.2012.6288385) (PDF)
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.
|| 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.
|| 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)
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]
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)
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
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.
This track compares retrieval performance of deformable / articulated 3D
shapes. So a global shape descriptor geared for a rigid shape is not useful.
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
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.
Shape-based Autotagging of 3D Models for Retrieval
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.
|| 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,
Scale-Weighted Dense Bag of Visual Features for 3D Model Retrieval from a Partial View 3D Model
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%.
|| 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
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.
|| 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)(doi>10.1145/1646396.1646430) (Recognized as one of "Most cited papers before the era of ICMR" in ACM SIGMM Records, Volume 6, Issue 1, March 2014 (ISSN 1947-4598).
Accelerating Bag-of-Features SIFT Algorithm for 3D Model Retrieval
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.
|| 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
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.
|| 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
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.
||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.
|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
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
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. This paper has received quite a few citations (i.e., >170, as of April, 2014) according to Google Scholar.
|| 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)
Off-line supervised learning of semantic categories for semantic 3D model
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．
|| 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
||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
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．
||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
Our paper that comapred the dimension reduction methods have been presented
at the AMR 2007 workshop in Paris, France in July.
||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".
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,
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
From January 1994 to March 1999, I worked for
Tokyo Research Laboratory. There, I worked in Advanced
Graphics and CAD group. I enjoyed working there, with Masaki Aono,
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. .
Ryutarou Ohbuchi （Japanese -> 大渕 竜太郎）
Computer Science Department
University of Yamanashi
4-3-11 Takeda, Kofu-shi,
my_last_name AT yamanashi DOT ac DOT jp