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.

翻译：语义场景补全（SSC）旨在联合生成复杂三维场景的空间占用与语义标签。现有SSC模型多基于体素表征，难以高效处理大型户外空间内存开销问题。点云作为轻量化替代方案，但现有基准缺乏带语义标签的户外点云场景。为此，我们提出PointSSC——首个面向语义场景补全的车路协同点云基准。这些场景具备远距离感知与极低遮挡特性。我们开发了基于语义分割一切模型（Semantic Segment Anything）的自动标注流程以实现高效语义分配。为推进基准进展，我们提出基于激光雷达的模型，其包含用于全局与局部特征提取的空间感知Transformer（Spatial-Aware Transformer）以及用于联合补全与分割的完成分割协作模块（Completion and Segmentation Cooperative Module）。PointSSC为驱动真实环境导航中语义点云补全技术突破提供了具有挑战性的试验平台。代码与数据集详见https://github.com/yyxssm/PointSSC。