PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

TL;DR

PointGroup introduces a novel bottom-up 3D instance segmentation method that leverages dual-set point grouping and a specialized scoring mechanism, significantly improving accuracy on challenging datasets.

Contribution

The paper proposes a new end-to-end architecture with dual-set point grouping and ScoreNet for better 3D instance segmentation in point clouds, outperforming previous methods.

Findings

Achieves state-of-the-art mAP scores on ScanNet v2 and S3DIS datasets.

Effectively utilizes both original and offset-shifted point sets for improved clustering.

Demonstrates significant performance gains over previous solutions.

Abstract

Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best solutions in terms of mAP with IoU threshold 0.5.