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

用深度学习方法，采用 O-CNN 和 U-Net 结构，在保留输入几何形状和从数据中学习几何形状之前，通过引入新的输出引导跳过连接的网络结构对输入的嘈杂和不完整形状或场景进行了完整性处理，并在三维形状完整性和语义场景计算方面取得了最先进的结果。