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 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.

实现语义场景补全 (SSC) 的 PointSSC 是首个针对语义场景补全的车辆基础设施合作点云基准，其利用点云作为轻量级替代方案，旨在提供语义标签的室外点云场景基准测试，准确度量验证真实导航中语义点云补全的进展。