Scribble-Supervised LiDAR Semantic Segmentation
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.
Code (3)
Tasks
3D Semantic SegmentationLIDAR Semantic SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing w…
2D Semantic Segmentation3D Semantic SegmentationContrastive LearningLIDAR Semantic Segmentation+2LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Densely annotating LiDAR point clouds is costly, which restrains the scalability of fully-supervised learning methods. In this work, we study the underexplored semi-supervised learning (SSL) in LiDAR segmentation. Our co…
LIDAR Semantic SegmentationSegmentationSemantic SegmentationSemi-Supervised Semantic SegmentationScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, sc…
Image SegmentationSegmentationSemantic SegmentationScribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
In this work, we introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. Training or fine-tuning semantic segmentation models with weak supervisio…
AllBenchmarkingSegmentationSemantic Segmentation+1Label-efficient Semantic Scene Completion with Scribble Annotations
Semantic scene completion aims to infer the 3D geometric structures with semantic classes from camera or LiDAR, which provide essential occupancy information in autonomous driving. Prior endeavors concentrate on construc…
Autonomous Driving