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Papers/Scribble-Supervised LiDAR Semantic Segmentation

Scribble-Supervised LiDAR Semantic Segmentation

Ozan Unal, Dengxin Dai, Luc van Gool

2022-03-16CVPR 2022 1SegmentationSemantic Segmentation3D Semantic SegmentationLIDAR Semantic Segmentation
PaperPDFCode(official)CodeCode

Abstract

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 pipeline to reduce the performance gap that arises when using such weak annotations. Our pipeline comprises of three stand-alone contributions that can be combined with any LiDAR semantic segmentation model to achieve up to 95.7% of the fully-supervised performance while using only 8% labeled points. Our scribble annotations and code are available at github.com/ouenal/scribblekitti.

Results

TaskDatasetMetricValueModel
Semantic SegmentationScribbleKITTImIoU61.3SSLSS with Cylinder3D
3D Semantic SegmentationScribbleKITTImIoU61.3SSLSS with Cylinder3D
10-shot image generationScribbleKITTImIoU61.3SSLSS with Cylinder3D

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