Scribble-Supervised LiDAR Semantic Segmentation

TL;DR

This paper introduces a new weakly-supervised approach for LiDAR semantic segmentation using scribble annotations, along with a dataset and a pipeline that achieves near fully-supervised performance with minimal labeled data.

Contribution

It presents the first scribble-annotated LiDAR dataset and a versatile pipeline that significantly reduces annotation effort while maintaining high segmentation accuracy.

Findings

Achieves up to 95.7% of fully-supervised performance

Uses only 8% of labeled points for training

Provides a new dataset and code for weakly-supervised LiDAR segmentation

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.