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We introduce a large-scale 3D shape understanding benchmark using data andannotation from ShapeNet 3D object database. The benchmark consists of twotasks: part-level segmentation of 3D shapes and 3D reconstruction from singleview images. Ten teams have participated in the challenge and the bestperforming teams have outperformed state-of-the-art approaches on both tasks. Afew novel deep learning architectures have been proposed on various 3Drepresentations on both tasks. We report the techniques used by each team andthe corresponding performances. In addition, we summarize the major discoveriesfrom the reported results and possible trends for the future work in the field.

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