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Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
[article]
2022
arXiv
pre-print
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
arXiv:2201.02963v1
fatcat:yfn3uqzqqfhgtkmj4m7jo6bs5e