Photometric Loss

Photometric loss is a crucial component in training computer vision models for tasks like depth estimation and 3D reconstruction, leveraging the consistency of image appearance across different views or time steps. Current research focuses on mitigating limitations of photometric loss, particularly its sensitivity to motion blur, varying lighting conditions (especially at night), and the inherent shape-radiance ambiguity in representing 3D scenes. This involves developing novel loss functions that incorporate velocity information, physical priors, or frequency-aware filtering, as well as refining model architectures like neural radiance fields (NeRFs) and bi-projection fusion methods. Improvements in photometric loss techniques are vital for advancing applications such as autonomous driving, virtual reality, and high-fidelity 3D modeling.