Open access
Date
2022Type
- Conference Paper
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000583568Publication status
publishedExternal links
Book title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Pages / Article No.
Publisher
IEEEEvent
Subject
Segmentation; grouping and shape analysis; Navigation and autonomous driving; Self-& semi-& meta- & unsupervised learningOrganisational unit
03514 - Van Gool, Luc / Van Gool, Luc
Related publications and datasets
Is referenced by: https://doi.org/10.3929/ethz-b-000668173
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