港科广LiSTAR：自动驾驶4D LiDAR世界模型！

Group 1 - The article discusses the challenges in generating high-fidelity 4D LiDAR data for autonomous driving simulations, highlighting issues with sensor characteristics, data sparsity, and controllability [2][4][8] - It introduces a novel hybrid cylindrical-spherical (HCS) coordinate voxelization method that addresses the inherent defects of Cartesian coordinates, allowing for efficient 4D data encoding while preserving geometric details [9] - The article presents the Ray-Centric World Models for 4D LiDAR sequences, emphasizing the importance of spatiotemporal attention mechanisms in modeling LiDAR sequences and ensuring temporal coherence [10][12] Group 2 - The MaskSTART framework is proposed for precise scene synthesis, utilizing a 4D point cloud alignment voxel layout as conditional input to enhance control over scene structure [12][20] - Experimental results demonstrate significant improvements in reconstruction, prediction, and generation tasks using the proposed methods, with metrics showing a 32% increase in IoU and a 60% reduction in MMD compared to baseline methods [21][22][28] - Ablation studies validate the effectiveness of the HCS coordinate system and the collaborative value of the spatial ray attention (SRA) and causal spatiotemporal attention (CSTA) modules in enhancing model performance [30][31]