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Acquiring complete and clean 3D shape and scene data is challenging due togeometric occlusion and insufficient views during 3D capturing. We present asimple yet effective deep learning approach for completing the input noisy andincomplete shapes or scenes. Our network is built upon the octree-based CNNs(O-CNN) with U-Net like structures, which enjoys high computational and memoryefficiency and supports to construct a very deep network structure for 3D CNNs.A novel output-guided skip-connection is introduced to the network structurefor better preserving the input geometry and learning geometry prior from dataeffectively. We show that with these simple adaptions -- output-guidedskip-connection and deeper O-CNN (up to 70 layers), our network achievesstate-of-the-art results in 3D shape completion and semantic scene computation.

Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion