Depth Completion
Overview
Depth completion fills in missing depth values to produce dense scene geometry from sparse measurements such as LiDAR, time-of-flight sensors, or radar, often combined with camera images. Common approaches fuse image and depth features, propagate measurements with learned or geometric rules, or use transformer, diffusion, and pretrained depth models; test-time adaptation can further align predictions to a particular scene. Methods are evaluated on benchmarks such as KITTI and NYUv2, as well as in settings including robotics, adverse weather, and specialized cameras. A central challenge is balancing metric accuracy and fine structure with robustness to noisy or irregular inputs, new sensors and domains, and real-time constraints; sparse ground truth also limits evaluation.
Latest papers
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