LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving

Synthesizing high-fidelity and controllable 4D LiDAR datais crucial for creating scalable simulation environments for autonomousdriving. This task is inherently challenging due to the sensor’s uniquespherical geometry, the temporal sparsity of point clouds, and the com-plexity of dynamic scenes. To address these challenges, we present LiS-TAR, a novel generative world model that operates directly on the sen-sor’s native geometry. LiSTAR introduces a Hybrid-Cylindrical-Spherical(HCS) representation to preserve data fidelity by mitigating quanti-zation artifacts common in Cartesian grids. To capture complex dy-namics from sparse temporal data, it utilizes a Spatio-Temporal At-tention with Ray-Centric Transformer (START) that explicitly modelsfeature evolution along individual sensor rays for robust temporal co-herence. Furthermore, for controllable synthesis, we propose a novel 4Dpoint cloud-aligned voxel layout for conditioning and a correspondingdiscrete Masked Generative START (MaskSTART) framework, whichlearns a compact, tokenized representation of the scene, enabling effi-cient, high-resolution, and layout-guided compositional generation. Com-prehensive experiments validate LiSTAR’s state-of-the-art performanceacross 4D LiDAR reconstruction, prediction, and conditional genera-tion. Our method achieves substantial quantitative gains: improving re-construction IoU by up to 85%, lowering prediction L1 Med by up to64%, and reducing generation MMD by 61%. This level of performanceprovides a powerful new foundation for creating realistic and control-lable simulations for autonomous driving systems. Project Page: https://ocean-luna.github.io/LiSTAR.github.io/.