Scribble-Supervised LiDAR Semantic Segmentation
This addresses the annotation bottleneck for LiDAR semantic segmentation in autonomous driving, offering a practical solution with incremental improvements.
The paper tackles the problem of expensive dense annotation for LiDAR point clouds by proposing scribble-based weak supervision, achieving up to 95.7% of fully-supervised performance with only 8% labeled points.
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.