Fast and Scalable LiDAR Data Generation for Autonomous Driving Simulation without Raycasting

Autonomous driving relies on LiDAR simulation for gener-ating realistic 3D point clouds, typically achieved using computation-ally expensive raycasting. We introduce GnoSphere360, a scalable Li-DAR data generation method that eliminates raycasting by projectingtriangular meshes onto a unit sphere and interpolating point sampleswith gnomonic projection. This computational approach bypasses tra-ditional ray-mesh intersection, significantly reducing computation timewhile maintaining compatibility for further downstream physics-basedmodeling. Benchmarking against NVIDIA OptiX and native CARLAengines, GnoSphere360 achieves up to 35× speedup over CARLA forphysical LiDAR models and maintains robust performance against Op-tiX, while preserving similar LiDAR point cloud output. However, forextremely dense LiDAR configurations, GnoSphere360 achieves over 3.6xspeedup against OptiX, showcasing the scalability of our proposed method.Ablation studies demonstrate robustness of GnoSphere360 across vary-ing scenes and sensor models, while preserving data fidelity. Our resultsshow GnoSphere360 is an efficient, accurate, and parallelizable alterna-tive to raycasting and rasterization for autonomous driving simulation.Project website: gnosphere360.github.io