  We introduce Spatial Group Convolution (SGC) for accelerating the computation
of 3D dense prediction tasks. SGC is orthogonal to group convolution, which
works on spatial dimensions rather than feature channel dimension. It divides
input voxels into different groups, then conducts 3D sparse convolution on
these separated groups. As only valid voxels are considered when performing
convolution, computation can be significantly reduced with a slight loss of
accuracy. The proposed operations are validated on semantic scene completion
task, which aims to predict a complete 3D volume with semantic labels from a
single depth image. With SGC, we further present an efficient 3D sparse
convolutional network, which harnesses a multiscale architecture and a
coarse-to-fine prediction strategy. Evaluations are conducted on the SUNCG
dataset, achieving state-of-the-art performance and fast speed. Code is
available at https://github.com/zjhthu/SGC-Release.git
