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@ -6,6 +6,11 @@ We provide code for reproducing results of the paper [**SoftGroup for 3D Instanc
Author: Thang Vu, Kookhoi Kim, Tung M. Luu, Xuan Thanh Nguyen, and Chang D. Yoo.
## Update
- 16/Apr/2022: The code base is refactored. Coding is more extendable, readable, and consistent. The following features are supported:
- Support up-to-date pytorch 1.11 and spconv 2.1.
- Support distributed and mix precision training. Training time on ScanNet v2 (on 4GPUs) reduces from 4 day to 10 hours.
## Introduction
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 of AP_50.