Hierarchical Aggregation for 3D Instance Segmentation

Understand

Instance segmentation on point clouds is a fundamental task in 3D scene perception.

• In this work, we propose a concise clustering-based framework named HAIS, which makes full use of spatial relation of points and point sets.
• Considering clustering-based methods may result in over-segmentation or under-segmentation, we introduce the hierarchical aggregation to progressively generate instance proposals, i.e., point aggregation for preliminarily clustering points to sets and set aggregation for generating complete instances from sets.
• Once the complete 3D instances are obtained, a sub-network of intra-instance prediction is adopted for noisy points filtering and mask quality scoring.