Learning Regional Purity for Instance Segmentation on 3D Point Clouds

3D instance segmentation is a fundamental task for scene understanding, with a variety of applications in robotics and AR/VR. Many proposal-free methods have been proposed recently for this task, with remarkable results and high efficiency. However, these methods heavily rely on instance centroid regression and do not explicitly detect object boundaries, thus may mistakenly group nearby objects into the same clusters in some scenarios. In this paper, we define a novel concept of “regional purity” as the percentage of neighboring points belonging to the same instance within a fixed-radius 3D space. Intuitively, it indicates the likelihood of a point belonging to the boundary area. To evaluate the feasibility of predicting regional purity, we design a strategy to build a random scene toy dataset based on existing training data. Besides, using toy data is a “free” way of data augmentation on learning regional purity, which eliminates the burdens of additional real data. We propose Regional Purity Guided Network (RPGN), which has separate branches for predicting semantic class, regional purity, offset, and size. Predicted regional purity information is utilized to guide our clustering algorithm. Experimental results demonstrate that using regional purity can simultaneously prevent under-segmentation and over-segmentation problems during clustering.



Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
3D Instance Segmentation ScanNet(v2) RPGN mAP 42.8 # 17
mAP @ 50 64.2 # 15
mAP@25 80.6 # 10


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