LiDAR Adverse-Weather Simulation with Ground Effect for Robust 3D Object Detection

LiDAR-based 3D object detection is critical for autonomous driving perception. Ensuring robust sensing under adverse weather is essential for safe deployment. Current physics-based simulation methods focus on atmospheric effects but offer limited ground-level modeling, leading to domain gaps between simulated and real-world snowy data. Ground-level effects are challenging to model due to diverse physical interactions: wet surface reflectivity changes, vehicle-induced spray, and multi-layer snow scattering. This paper proposes a simulation method with more comprehensive ground-effect modeling for snowfall scenarios. Our approach introduces two modules: (i) an extended spray model with precipitation-controlled parameters that jointly models spray noise and wet ground attenuation, and (ii) a multi-layer dual-mode backscattering model that captures both diffuse and specular reflections on snow-covered ground. Both modules share a unified precipitation-driven parameterization. Higher snowfall rates simultaneously control spray generation, wet surface reflectivity, and snow accumulation depth. This design ensures physical consistency and makes the approach applicable across diverse LiDAR systems without sensor-specific tuning. Experiments on the STF dataset demonstrate consistent improvements over four state-of-the-art methods under both heavy and light snowfall. Clear-weather performance is preserved. Evaluations on roadside LiDAR further confirm generalizability to infrastructure-based scenarios.