“…Haldar et al used a physics-based simulator to boost the performance of an object detection algorithm in rain [10]. Many synthetic rain generation techniques are deep learning-based and rely on the collection of rain and no-rain image pairs to use as training data [11,12].…”
This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.
“…Haldar et al used a physics-based simulator to boost the performance of an object detection algorithm in rain [10]. Many synthetic rain generation techniques are deep learning-based and rely on the collection of rain and no-rain image pairs to use as training data [11,12].…”
This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.
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