  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.
