  As a voxel-wise labeling task, semantic scene completion (SSC) tries to
simultaneously infer the occupancy and semantic labels for a scene from a
single depth and/or RGB image. The key challenge for SSC is how to effectively
take advantage of the 3D context to model various objects or stuffs with severe
variations in shapes, layouts and visibility. To handle such variations, we
propose a novel module called anisotropic convolution, which properties with
flexibility and power impossible for the competing methods such as standard 3D
convolution and some of its variations. In contrast to the standard 3D
convolution that is limited to a fixed 3D receptive field, our module is
capable of modeling the dimensional anisotropy voxel-wisely. The basic idea is
to enable anisotropic 3D receptive field by decomposing a 3D convolution into
three consecutive 1D convolutions, and the kernel size for each such 1D
convolution is adaptively determined on the fly. By stacking multiple such
anisotropic convolution modules, the voxel-wise modeling capability can be
further enhanced while maintaining a controllable amount of model parameters.
Extensive experiments on two SSC benchmarks, NYU-Depth-v2 and NYUCAD, show the
superior performance of the proposed method. Our code is available at
https://waterljwant.github.io/SSC/
