Researchers Improve Monocular Depth Estimation Using Diffusion-Based Feature Restoration

A new approach treats single-image depth estimation as a feature restoration problem using diffusion models and invertible transforms, achieving 4-37% performance improvements on KITTI benchmarks. The method enhances encoder features through indirect supervision and auxiliary viewpoint data, advancing 3D vision applications in robotics and autonomous systems.