Multi-Sensor Alignment for Weather Simulations
arXiv:2607.25612
The paper introduces methods to align weather simulations across multiple sensors for autonomous vehicle perception, including ReDAM for fog intensity and Unified-weather-edit for rain and snow particle positioning, and demonstrates that aligned simulations improve the realism and robustness of 3D object detection models.
Abstract
Perception tasks for autonomous vehicles need to work satisfactorily in adverse weather conditions. Due to lack of real-world weather datasets, weather simulations are a promising alternative. To ensure simulations closely mirror real-world weather data, it's crucial that they represent the same weather characteristics, including severity and particle positioning, across different sensors. To achieve this, we propose the Reference Dataset Alignment Method (ReDAM) for weather intensity alignment in fog and Unified-weather-edit (inspired by Weather-edit[1]) for particle positioning alignment in rain and snow. We validate both alignment methods using statistical and geometrical tests, respectively. We find that 3D detection models for non-aligned versions tend to be overly optimistic as compared to aligned versions. We also show the aligned-multi-sensor simulation's effectiveness for achieving robustness for 3D object detection task by finetuning existing sensor fusion models on it.