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
this https URL

本研究提出了一种名为 Scribble2Scene 的简单而有效的方法，通过将稀疏的涂鸦标注与密集的几何标签相结合，从而弥补了稀疏涂鸦标注和完全监督之间的差距，实现了语义场景的完整性，并通过几何感知自动标注器建立和在线模型训练来提高性能。