CabNIR: A Benchmark for In-Vehicle Infrared Monocular Depth Estimation

Accurate in-cabin depth estimation is critical for advancing automotive safety and occupant comfort. However, existing datasets for in-vehicle scene understanding tasks often fall short in providing sufficient information and scale needed to evaluate existing depth estimation methods. In this paper, we present a novel benchmark tailored for monocular depth estimation in vehicle interiors, containing both near-infrared (NIR) images and corresponding ground truth depth data. Featuring over 41,000 frames captured across 36 distinct vehicles and 45 different passengers, it offers an unprecedented level of variability for this application domain. Evaluation on our testbench of cutting-edge single-view depth models in different flavors, including zero-shot affine-invariant depth estimation or indomain specialization, reveals that current depth estimation approaches, while promising, still have a significant performance gap to overcome before achieving the reliability required for downstream safety-critical applications. In light of its diverse range and complex scenarios, we believe this benchmark could serve as a common reference for further research concerning in-cabin monocular depth estimation.