SoftGroup for 3D Instance Segmentation on Point Clouds
This addresses the issue of low accuracy and false positives in 3D instance segmentation for applications like robotics and autonomous driving, representing a strong specific improvement.
The paper tackles the problem of error propagation in 3D instance segmentation on point clouds by proposing SoftGroup, which uses soft grouping and refinement to improve accuracy, resulting in performance gains of +6.2% on ScanNet v2 and +6.8% on S3DIS Area 5 in AP_50.
Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation followed by grouping. The hard predictions are made when performing semantic segmentation such that each point is associated with a single class. However, the errors stemming from hard decision propagate into grouping that results in (1) low overlaps between the predicted instance with the ground truth and (2) substantial false positives. To address the aforementioned problems, this paper proposes a 3D instance segmentation method referred to as SoftGroup by performing bottom-up soft grouping followed by top-down refinement. SoftGroup allows each point to be associated with multiple classes to mitigate the problems stemming from semantic prediction errors and suppresses false positive instances by learning to categorize them as background. Experimental results on different datasets and multiple evaluation metrics demonstrate the efficacy of SoftGroup. Its performance surpasses the strongest prior method by a significant margin of +6.2% on the ScanNet v2 hidden test set and +6.8% on S3DIS Area 5 in terms of AP_50. SoftGroup is also fast, running at 345ms per scan with a single Titan X on ScanNet v2 dataset. The source code and trained models for both datasets are available at \url{https://github.com/thangvubk/SoftGroup.git}.