Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision

• URL: http://arxiv.org/abs/2201.02963v1
• Date: Sun, 9 Jan 2022 09:07:48 GMT
• Title: Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
• Authors: Yan Liu, Qingyong Hu, Yinjie Lei, Kai Xu, Jonathan Li and Yulan Guo
• Abstract summary: We introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision. We show that the proposed network can be trained with cheap, or even off-the-shelf bounding box-level annotations and subcloud-level tags.
• Score: 65.19589997822155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly supervised methods can effectively learn semantics with only a small fraction of point-level annotations, we find that the vanilla bounding box-level annotation is also informative for semantic segmentation of large-scale 3D point clouds. In this paper, we introduce a neural architecture, termed Box2Seg, to learn point-level semantics of 3D point clouds with bounding box-level supervision. The key to our approach is to generate accurate pseudo labels by exploring the geometric and topological structure inside and outside each bounding box. Specifically, an attention-based self-training (AST) technique and Point Class Activation Mapping (PCAM) are utilized to estimate pseudo-labels. The network is further trained and refined with pseudo labels. Experiments on two large-scale benchmarks including S3DIS and ScanNet demonstrate the competitive performance of the proposed method. In particular, the proposed network can be trained with cheap, or even off-the-shelf bounding box-level annotations and subcloud-level tags.

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