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Scribble-Supervised LiDAR Semantic Segmentation
[article]
2022
arXiv
pre-print
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised performance, developing efficient methods that take advantage of realistic weak supervision have yet to be explored. In this paper, we propose using scribbles to annotate LiDAR point clouds and release ScribbleKITTI, the first scribble-annotated dataset for LiDAR semantic segmentation. Furthermore, we present a
arXiv:2203.08537v2
fatcat:jwqvpolcfvg7pmaxrvkm34xcwa