Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion
This work addresses the challenge of acquiring complete 3D data for applications in computer vision and graphics, representing an incremental improvement with specific architectural enhancements.
The paper tackles the problem of completing noisy and incomplete 3D shapes or scenes by introducing a deep learning approach based on octree-based CNNs with output-guided skip connections, achieving state-of-the-art results in 3D shape completion and semantic scene computation.
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.