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Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

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arxiv 1710.06104 v2 pith:4TK3FQSS submitted 2017-10-17 cs.CV

Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

Li Yi , Lin Shao , Manolis Savva , Haibin Huang , Yang Zhou , Qirui Wang , Benjamin Graham , Martin Engelcke
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Roman Klokov Victor Lempitsky Yuan Gan Pengyu Wang Kun Liu Fenggen Yu Panpan Shui Bingyang Hu Yan Zhang Yangyan Li Rui Bu Mingchao Sun Wei Wu Minki Jeong Jaehoon Choi Changick Kim Angom Geetchandra Narasimha Murthy Bhargava Ramu Bharadwaj Manda M Ramanathan Gautam Kumar P Preetham Siddharth Srivastava Swati Bhugra Brejesh Lall Christian Haene Shubham Tulsiani Jitendra Malik Jared Lafer Ramsey Jones Siyuan Li Jie Lu Shi Jin Jingyi Yu Qixing Huang Evangelos Kalogerakis Silvio Savarese Pat Hanrahan Thomas Funkhouser Hao Su Leonidas Guibas
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classification cs.CV
keywords tasksbenchmarklarge-scalereconstructionsegmentationshapeshapenetteams
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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    VT-3DAD fuses visual deviation from few-shot references and semantic deviation from textual normal space to achieve SOTA cross-category 3D anomaly detection on ShapeNetPart.