Boda Liu

Yuxiang Yan, Boda Liu, Jianfei Ai et al.

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.

Yifan Yang, Yuxiang Yan, Boda Liu et al.

Point clouds collected from real-world environments are often incomplete due to factors such as limited sensor resolution, single viewpoints, occlusions, and noise. These challenges make point cloud completion essential for various applications. A key difficulty in this task is predicting the overall shape and reconstructing missing regions from highly incomplete point clouds. To address this, we introduce CasPoinTr, a novel point cloud completion framework using cascaded networks and knowledge distillation. CasPoinTr decomposes the completion task into two synergistic stages: Shape Reconstruction, which generates auxiliary information, and Fused Completion, which leverages this information alongside knowledge distillation to generate the final output. Through knowledge distillation, a teacher model trained on denser point clouds transfers incomplete-complete associative knowledge to the student model, enhancing its ability to estimate the overall shape and predict missing regions. Together, the cascaded networks and knowledge distillation enhance the model's ability to capture global shape context while refining local details, effectively bridging the gap between incomplete inputs and complete targets. Experiments on ShapeNet-55 under different difficulty settings demonstrate that CasPoinTr outperforms existing methods in shape recovery and detail preservation, highlighting the effectiveness of our cascaded structure and distillation strategy.