  RGB images differentiate from depth images as they carry more details about
the color and texture information, which can be utilized as a vital
complementary to depth for boosting the performance of 3D semantic scene
completion (SSC). SSC is composed of 3D shape completion (SC) and semantic
scene labeling while most of the existing methods use depth as the sole input
which causes the performance bottleneck. Moreover, the state-of-the-art methods
employ 3D CNNs which have cumbersome networks and tremendous parameters. We
introduce a light-weight Dimensional Decomposition Residual network (DDR) for
3D dense prediction tasks. The novel factorized convolution layer is effective
for reducing the network parameters, and the proposed multi-scale fusion
mechanism for depth and color image can improve the completion and segmentation
accuracy simultaneously. Our method demonstrates excellent performance on two
public datasets. Compared with the latest method SSCNet, we achieve 5.9% gains
in SC-IoU and 5.7% gains in SSC-IOU, albeit with only 21% network parameters
and 16.6% FLOPs employed compared with that of SSCNet.
