  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.
