  Semantic Scene Completion (SSC) aims to jointly generate space occupancies
and semantic labels for complex 3D scenes. Most existing SSC models focus on
volumetric representations, which are memory-inefficient for large outdoor
spaces. Point clouds provide a lightweight alternative but existing benchmarks
lack outdoor point cloud scenes with semantic labels. To address this, we
introduce PointSSC, the first cooperative vehicle-infrastructure point cloud
benchmark for semantic scene completion. These scenes exhibit long-range
perception and minimal occlusion. We develop an automated annotation pipeline
leveraging Semantic Segment Anything to efficiently assign semantics. To
benchmark progress, we propose a LiDAR-based model with a Spatial-Aware
Transformer for global and local feature extraction and a Completion and
Segmentation Cooperative Module for joint completion and segmentation. PointSSC
provides a challenging testbed to drive advances in semantic point cloud
completion for real-world navigation. The code and datasets are available at
https://github.com/yyxssm/PointSSC.
