Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Semantic Scene Completion Network with Spatial Group
  Convolution   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

17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
16
The categories of this paper are: stat
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex