3D Mesh Unfolding via Semidefinite Programming (Unknown language)

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Mesh unfolding is a powerful pre-processing tool for many tasks such as non-rigid shape matching and retrieval. Shapes with articulated parts may exist large variants in pose, which brings difficulties to those tasks. With mesh unfolding, shapes in different poses can be transformed into similar canonical forms, which facilitates the subsequent applications. In this paper, we propose an automatic mesh unfolding algorithm based on semidefinite programming. The basic idea is to maximize the total variance of the vertex set for a given 3D mesh, while preserving the details by minimizing locally linear reconstruction errors. By optimizing a specifically-designed objective function, vertices tend to move against each other as far as possible, which leads to the unfolding operation. Compared to other Multi-Dimensional Scaling (MDS) based unfolding approaches, our method preserves significantly more details and requires no geodesic distance calculation. We demonstrate the advantages of our algorithm by performing 3D shape matching and retrieval in two publicly available datasets. Experimental results validate the effectiveness of our method both in visual judgment and quantitative comparison.

Table of contents conference proceedings

The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

1
Exploiting the PANORAMA Representation for Convolutional Neural Network Classification and Retrieval
Sfikas, Konstantinos / Theoharis, Theoharis / Pratikakis, Ioannis | 2017
9
LightNet: A Lightweight 3D Convolutional Neural Network for Real-Time 3D Object Recognition
Zhi, Shuaifeng / Liu, Yongxiang / Li, Xiang / Guo, Yulan | 2017
17
Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
Boulch, Alexandre / Saux, Bertrand Le / Audebert, Nicolas | 2017
25
RGB-D to CAD Retrieval with ObjectNN Dataset
Hua, Binh-Son / Truong, Quang-Trung / Tran, Minh-Khoi / Pham, Quang-Hieu / Kanezaki, Asako / Lee, Tang / Chiang, HungYueh / Hsu, Winston / Li, Bo / Lu, Yijuan et al. | 2017
33
3D Hand Gesture Recognition Using a Depth and Skeletal Dataset
Smedt, Quentin De / Wannous, Hazem / Vandeborre, Jean-Philippe / Guerry, J. / Saux, B. Le / Filliat, D. | 2017
39
Large-Scale 3D Shape Retrieval from ShapeNet Core55
Savva, Manolis / Yu, Fisher / Su, Hao / Kanezaki, Asako / Furuya, Takahiko / Ohbuchi, Ryutarou / Zhou, Zhichao / Yu, Rui / Bai, Song / Bai, Xiang et al. | 2017
51
Shape Similarity System driven by Digital Elevation Models for Non-rigid Shape Retrieval
Craciun, Daniela / Levieux, Guillaume / Montes, Matthieu | 2017
55
Sketch-based 3D Object Retrieval with Skeleton Line Views - Initial Results and Research Problems
Zhao, Xueqing / Gregor, Robert / Mavridis, Pavlos / Schreck, Tobias | 2017
59
GSHOT: a Global Descriptor from SHOT to Reduce Time and Space Requirements
Mateo, Carlos M. / Gil, Pablo / Torres, Fernando | 2017
63
A Framework Based on Compressed Manifold Modes for Robust Local Spectral Analysis
Haas, Sylvain / Baskurt, Atilla / Dupont, Florent / Denis, Florence | 2017
67
Protein Shape Retrieval
Song, Na / Craciun, Daniela / Christoffer, Charles W. / Han, Xusi / Kihara, Daisuke / Levieux, Guillaume / Montes, Matthieu / Qin, Hong / Sahu, Pranjal / Terashi, Genki et al. | 2017
75
Point-Cloud Shape Retrieval of Non-Rigid Toys
Limberger, F. A. / Wilson, R. C. / Aono, M. / Audebert, N. / Boulch, A. / Bustos, B. / Giachetti, A. / Godil, A. / Saux, B. Le / Li, B. et al. | 2017
85
Deformable Shape Retrieval with Missing Parts
Rodolà, E. / Cosmo, L. / Litany, O. / Bronstein, M. M. / Bronstein, A. M. / Audebert, N. / Hamza, A. Ben / Boulch, A. / Castellani, U. / Do, M. N. et al. | 2017
95
Retrieval of Surfaces with Similar Relief Patterns
Biasotti, S. / Thompson, E. Moscoso / Aono, M. / Hamza, A. Ben / Bustos, B. / Dong, S. / Du, B. / Fehri, A. / Li, H. / Limberger, F. A. et al. | 2017
105
3D Mesh Unfolding via Semidefinite Programming
Liu, Juncheng / Lian, Zhouhui / Xiao, Jianguo | 2017
113
Directed Curvature Histograms for Robotic Grasping
Schulz, Rodrigo / Guerrero, Pablo / Bustos, Benjamin | 2017
121
Semantic Correspondence Across 3D Models for Example-based Modeling
Léon, Vincent / Itier, Vincent / Bonneel, Nicolas / Lavoué, Guillaume / Vandeborre, Jean-Philippe | 2017
129
Towards Recognizing of 3D Models Using A Single Image
Rashwan, Hatem A. / Chambon, Sylvie / Morin, Geraldine / Gurdjos, Pierre / Charvillat, Vincent | 2017
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