AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation

LiDAR sensors provide rich 3D information about their surrounding and are becoming increasingly important for autonomous vehicles tasks, such as semantic segmentation, object detection, and tracking. Simulating a LiDAR sensor accelerates the testing, validation, and deployment of autonomous vehicles, while reducing the cost and eliminating the risks of testing in real-world scenarios. We address the problem of high-fidelity LiDAR simulation and present a pipeline that leverages real-world point clouds acquired by mobile mapping systems. Point-based geometry representations, more specifically splats, have proven their ability to accurately model the underlying surface in very large point clouds. We introduce an adaptive splats generation method that accurately models the underlying 3D geometry, especially for thin structures. Moreover, we introduce a physics-based, faster-than-real-time LiDAR simulator, in the splatted model, leveraging the GPU parallel architecture with an acceleration structure, while focusing on efficiently handling large point clouds. We test our LiDAR simulation in real-world conditions, showing qualitative and quantitative results compared to basic splatting and meshing techniques, demonstrating the interest of our modeling technique.