  We propose a new approach for 3D instance segmentation based on sparse
convolution and point affinity prediction, which indicates the likelihood of
two points belonging to the same instance. The proposed network, built upon
submanifold sparse convolution [3], processes a voxelized point cloud and
predicts semantic scores for each occupied voxel as well as the affinity
between neighboring voxels at different scales. A simple yet effective
clustering algorithm segments points into instances based on the predicted
affinity and the mesh topology. The semantic for each instance is determined by
the semantic prediction. Experiments show that our method outperforms the
state-of-the-art instance segmentation methods by a large margin on the widely
used ScanNet benchmark [2]. We share our code publicly at
https://github.com/art-programmer/MASC.
