Simulation-Based Evaluation of LiDAR–Photogrammetry Fusion via NeRF Reconstruction and ICP Registration in Urban Scenes

Reliable integration of LiDAR and photogrammetric point clouds is essential for high-precision 3D mapping, yet systematic evaluations of fusion accuracy under controlled conditions remain limited. This study presents a simulation-based assessment framework for LiDAR–photogrammetry fusion using a neural radiance field (NeRF) representation and iterative closest point (ICP) registration. Experiments were conducted in the CARLA simulator (Town 10), where a drone-mounted multi-sensor platform (RGB, LiDAR, GPS, IMU) generated spatially aligned datasets. Photogrammetric reconstructions were produced using the Nerfstudio–Nerfacto pipeline with varied architectural and sampling configurations to analyze their impact on cross-modal registration. Quantitative evaluation employed Chamfer and cloud-to-cloud (C2C) distances to assess NeRF reconstruction fidelity, and ICP root-mean-square error (RMSE), inlier fitness, and runtime to evaluate registration performance. Results show that tuning NeRF’s hidden dimensions and sampling levels yields up to 25% lower ICP RMSE and faster convergence across object categories. The proposed framework enables reproducible benchmarking of LiDAR–photogrammetry fusion and provides a foundation for extending NeRF-based methods to real-world urban mapping scenarios.