Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best p...
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| creator | Li, Yi Shao, Lin Savva, Manolis Huang, Haibin Zhou, Yang Wang, Qirui Graham, Benjamin Engelcke, Martin Klokov, Roman Lempitsky, Victor Gan, Yuan Wang, Pengyu Liu, Kun Yu, Fenggen Panpan Shui Hu, Bingyang Zhang, Yan Li, Yangyan Bu, Rui Sun, Mingchao Wu, Wei Jeong, Minki Choi, Jaehoon Kim, Changick Angom Geetchandra Murthy, Narasimha Bhargava Ramu Bharadwaj Manda Ramanathan, M Kumar, Gautam Preetham, P Srivastava, Siddharth Bhugra, Swati Lall, Brejesh Haene, Christian Tulsiani, Shubham Malik, Jitendra Lafer, Jared Jones, Ramsey Li, Siyuan Lu, Jie Shi, Jin Yu, Jingyi Huang, Qixing Kalogerakis, Evangelos Savarese, Silvio Hanrahan, Pat Funkhouser, Thomas Su, Hao Guibas, Leonidas |
| description | We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field. |
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The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Annotations ; Benchmarks ; Image reconstruction ; Image segmentation ; Machine learning</subject><ispartof>arXiv.org, 2017-10</ispartof><rights>2017. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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Yang ; Wang, Qirui ; Graham, Benjamin ; Engelcke, Martin ; Klokov, Roman ; Lempitsky, Victor ; Gan, Yuan ; Wang, Pengyu ; Liu, Kun ; Yu, Fenggen ; Panpan Shui ; Hu, Bingyang ; Zhang, Yan ; Li, Yangyan ; Bu, Rui ; Sun, Mingchao ; Wu, Wei ; Jeong, Minki ; Choi, Jaehoon ; Kim, Changick ; Angom Geetchandra ; Murthy, Narasimha ; Bhargava Ramu ; Bharadwaj Manda ; Ramanathan, M ; Kumar, Gautam ; Preetham, P ; Srivastava, Siddharth ; Bhugra, Swati ; Lall, Brejesh ; Haene, Christian ; Tulsiani, Shubham ; Malik, Jitendra ; Lafer, Jared ; Jones, Ramsey ; Li, Siyuan ; Lu, Jie ; Shi, Jin ; Yu, Jingyi ; Huang, Qixing ; Kalogerakis, Evangelos ; Savarese, Silvio ; Hanrahan, Pat ; Funkhouser, Thomas ; Su, Hao ; Guibas, 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Haibin</au><au>Zhou, Yang</au><au>Wang, Qirui</au><au>Graham, Benjamin</au><au>Engelcke, Martin</au><au>Klokov, Roman</au><au>Lempitsky, Victor</au><au>Gan, Yuan</au><au>Wang, Pengyu</au><au>Liu, Kun</au><au>Yu, Fenggen</au><au>Panpan Shui</au><au>Hu, Bingyang</au><au>Zhang, Yan</au><au>Li, Yangyan</au><au>Bu, Rui</au><au>Sun, Mingchao</au><au>Wu, Wei</au><au>Jeong, Minki</au><au>Choi, Jaehoon</au><au>Kim, Changick</au><au>Angom Geetchandra</au><au>Murthy, Narasimha</au><au>Bhargava Ramu</au><au>Bharadwaj Manda</au><au>Ramanathan, M</au><au>Kumar, Gautam</au><au>Preetham, P</au><au>Srivastava, Siddharth</au><au>Bhugra, Swati</au><au>Lall, Brejesh</au><au>Haene, Christian</au><au>Tulsiani, Shubham</au><au>Malik, Jitendra</au><au>Lafer, Jared</au><au>Jones, Ramsey</au><au>Li, Siyuan</au><au>Lu, Jie</au><au>Shi, Jin</au><au>Yu, Jingyi</au><au>Huang, Qixing</au><au>Kalogerakis, Evangelos</au><au>Savarese, Silvio</au><au>Hanrahan, Pat</au><au>Funkhouser, Thomas</au><au>Su, 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| subjects | Annotations Benchmarks Image reconstruction Image segmentation Machine learning |
| title | Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 |
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