Framework for Low-Density Point Cloud Processing and 3D Mesh Simulation in Autonomous Vehicle Environments

This paper proposes a new framework capable of transforming low-density point cloud data (PCD) into high- quality, real-time 3D meshes to simulate autonomous vehicles, which can solve the urgent need for high-fidelity 3D environments and computationally efficient obstacle detection. The proposed framework applies advanced techniques such as Statistical Outlier Removal (SOR), Delaunay Triangulation, and Poisson Surface Reconstruction to reconstruct realistic and noise-free environmental models and optimize computational efficiency. First low-density PCD is subjected to noise filtering through SOR of CloudCompare to eliminate external noise and redundant points. Delaunay Triangulation method is used to add new points in the triangular mesh through interpolation, which raises point density and data quality drastically. Poisson Surface Reconstruction is used next to improve the mesh to build gap-free, smooth, high-density 3D model. Synthetic 3D meshes are elegantly integrated within the CARLA simulation platform, with smooth car motion through spline interpolation over carefully devised routes. Real-time obstacle detection sensors and URDF-based car model are utilized in the CARLA platform in a bid to simulate advanced navigation challenges and test autonomous vehicle algorithms with realistic real-time perception. This methodology augments realism and computation savings in autonomous vehicle testing through detailed pathway mapping and awareness of surroundings within a simulated virtual world. System effectiveness is evidenced through the effective transformation of low-density PCD to high-density 3D meshes in order to enable realistic and intricate simulation in CARLA. The tremendous demand for correct 3D models within autonomous navigation systems renders efficient processing of point clouds absolutely necessary. Our approach produces an impressive noise reduction and improves reconstruction accuracy to ensure solid obstacle detection capability.