Deep Denoising for Multiview Depth Cameras

A novel method for noise removal in multicamera depth sensing systems is proposed in this work. The method uses a combination of convolutional neural networks applied on each depth camera separately, followed by depth reprojection and joint processing of the resulting point clouds using a 3-D neural network. Both depth and point cloud processing networks are designed to preserve the structure of the depth maps by appropriately correcting noise in the direction of the viewing rays. The proposed method accommodates any depth unit and noise intensity, thanks to adequate normalization in both the processing steps. The proposed approach is shown to outperform the state-of-the-art methods for both synthetic and real data captured with a multicamera setup and can reduce intercamera inconsistencies while preserving depth map structures.