MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation

Abstract

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.

References in corpus (1)

• Submanifold Sparse Convolutional Networks

Cited by in corpus (6)

• PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
• Identifying Unknown Instances for Autonomous Driving
• Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
• OccuSeg: Occupancy-aware 3D Instance Segmentation
• Multi-view PointNet for 3D Scene Understanding
• SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization