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.

翻译：语义场景补全（Semantic Scene Completion, SSC）旨在针对复杂三维场景联合生成空间占用状态与语义标签。现有SSC模型多聚焦于体素表征，该方式在处理大型户外场景时存在内存效率低下的缺陷。点云提供了一种轻量级替代方案，但现有基准数据集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数据集。该数据集场景具备远距离感知和最小化遮挡的特点。我们开发了一种基于Segment Anything的自动化标注管线，能够高效地赋予语义信息。为评估研究进展，我们提出一种基于激光雷达的模型，该模型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与分割协作模块（Completion and Segmentation Cooperative Module）。PointSSC为推进面向实际场景导航的语义点云补全技术发展提供了具有挑战性的测试平台。