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RGB images differentiate from depth images as they carry more details aboutthe color and texture information, which can be utilized as a vitalcomplementary to depth for boosting the performance of 3D semantic scenecompletion (SSC). SSC is composed of 3D shape completion (SC) and semanticscene labeling while most of the existing methods use depth as the sole inputwhich causes the performance bottleneck. Moreover, the state-of-the-art methodsemploy 3D CNNs which have cumbersome networks and tremendous parameters. Weintroduce a light-weight Dimensional Decomposition Residual network (DDR) for3D dense prediction tasks. The novel factorized convolution layer is effectivefor reducing the network parameters, and the proposed multi-scale fusionmechanism for depth and color image can improve the completion and segmentationaccuracy simultaneously. Our method demonstrates excellent performance on twopublic datasets. Compared with the latest method SSCNet, we achieve 5.9% gainsin SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parametersand 16.6% FLOPs employed compared with that of SSCNet.

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion