Label-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 constructing the network or benchmark in a fully supervised manner. While the dense occupancy grids need point-wise semantic annotations, which incur expensive and tedious labeling costs. In this paper, we build a new label-efficient benchmark, named ScribbleSC, where the sparse scribble-based semantic labels are combined with dense geometric labels for semantic scene completion. In particular, we propose a simple yet effective approach called Scribble2Scene, which bridges the gap between the sparse scribble annotations and fully-supervision. Our method consists of geometric-aware auto-labelers construction and online model training with an offline-to-online distillation module to enhance the performance. Experiments on SemanticKITTI demonstrate that Scribble2Scene achieves competitive performance against the fully-supervised counterparts, showing 99% performance of the fully-supervised models with only 13. 5% voxels labeled. Both annotations of ScribbleSC and our full implementation are available at https: //github. com/songw-zju/Scribble2Scene.

Authors

• Song Wang York University
• Jiawei Yu
• Wentong Li Zhejiang University
• Hao Shi School of Computer Science and Technology, University of the Chinese Academy of Sciences, Beijing, China
• Kailun Yang School of Robotics, Hunan University
• Junbo Chen
• Jianke Zhu Zhejiang University

Keywords

• Computer Vision: CV: Applications
• Computer Vision: CV: Scene analysis and understanding