Learning part-based templates from large collections of 3D shapes-Reference-Cited by-同舟云学术

Learning part-based templates from large collections of 3D shapes

Published:2013-07-21 Issue:4 Volume:32 Page:1-12
ISSN:0730-0301
Container-title:ACM Transactions on Graphics
language:en
Short-container-title:ACM Trans. Graph.

Author:

Kim Vladimir G.¹,Li Wilmot²,Mitra Niloy J.³,Chaudhuri Siddhartha¹,DiVerdi Stephen⁴,Funkhouser Thomas¹

Affiliation:

1. Princeton University

2. Adobe Research

3. University College London

4. Adobe Research and Google

Funder

Adobe Systems

Intel Corporation

Google

Seventh Framework Programme

Division of Computer and Network Systems

Natural Sciences and Engineering Research Council of Canada

Division of Computing and Communication Foundations

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design

Link

https://dl.acm.org/doi/pdf/10.1145/2461912.2461933

Reference36 articles.

1. Amazon 2012. Amazon mechanical turk https://www.mturk.com/. Amazon 2012. Amazon mechanical turk https://www.mturk.com/.

2. POP: Patchwork of Parts Models for Object Recognition

3. Boykov Y. Veksler O. and Zabih R. 2001. Efficient approximate energy minimization via graph cuts. IEEE transactions on PAMI 20 12 1222--1239. 10.1109/34.969114 Boykov Y. Veksler O. and Zabih R. 2001. Efficient approximate energy minimization via graph cuts. IEEE transactions on PAMI 20 12 1222--1239. 10.1109/34.969114

4. Eslami S. M. A. and Williams C. 2012. A generative model for parts-based object segmentation. In NIPS. Eslami S. M. A. and Williams C. 2012. A generative model for parts-based object segmentation. In NIPS .

5. Pictorial Structures for Object Recognition

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