Learning-Based High Dynamic Range Imaging for Time-of-Flight Cameras

Time-of-flight (ToF) cameras provide cost-effective three-dimensional depth sensing but are constrained by limited dynamic range, hindering reliable performance in scenes with large variations in distance and reflectance. To overcome this limitation, this study proposes a deep learning-based high-dynamic range (HDR) approach, DeepToF-HDR. The method combines two key neural networks: an exposure time selection network (ESN) that dynamically adjusts scene-dependent exposure times, and a depth fusion network (DFN) that integrates multi-exposure-ToF measurements. A composite loss function with end-to-end joint training is employed to ensure stable and synergistic optimization of both networks. Under identical exposure-time configurations, experiments on a real multi-exposure ToF dataset show that DeepToF-HDR achieves a 54.1% reduction in the mean absolute error (MAE) of the depth compared with conventional baselines. Comparable accuracy is also achieved with less than half the number of captures and only 28% of the total exposure time, demonstrating superior accuracy and efficiency.