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Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
[article]
2020
arXiv
pre-print
3D reconstruction from a single RGB image is a challenging problem in computer vision. Previous methods are usually solely data-driven, which lead to inaccurate 3D shape recovery and limited generalization capability. In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image
arXiv:2006.10042v1
fatcat:cqscj5mzjzeepj7yx4bwxsuc7u