1 research outputs found
PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion
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.Comment: 8 pages, 5 figures, submitted to ICRA202