ISETHDR: A Physics-based Synthetic Radiance Dataset for High Dynamic Range Driving Scenes
This provides a tool for researchers in autonomous driving to test and optimize sensors in challenging HDR conditions, though it is incremental as it builds on existing simulation methods.
The paper tackles the problem of simulating high dynamic range (HDR) driving scenes by creating a physics-based synthetic radiance dataset and software, resulting in a labeled dataset with instance segmentation and depth, and a comparative analysis of two HDR sensors.
This paper describes a physics-based end-to-end software simulation for image systems. We use the software to explore sensors designed to enhance performance in high dynamic range (HDR) environments, such as driving through daytime tunnels and under nighttime conditions. We synthesize physically realistic HDR spectral radiance images and use them as the input to digital twins that model the optics and sensors of different systems. This paper makes three main contributions: (a) We create a labeled (instance segmentation and depth), synthetic radiance dataset of HDR driving scenes. (b) We describe the development and validation of the end-to-end simulation framework. (c) We present a comparative analysis of two single-shot sensors designed for HDR. We open-source both the dataset and the software.