CVPR 2023 - Shakes on a Plane: Unsupervised Depth Estimation

In this episode we discuss Shakes on a Plane: Unsupervised Depth Estimation by Authors: - Ilya Chugunov - Yuxuan Zhang - Felix Heide Affiliation: - Princeton University. The paper discusses a new method for recovering high-quality scene depth from long-burst sequences captured by mobile burst photography pipelines. The researchers investigate using natural hand tremor to obtain enough parallax information to recover scene depth. They introduce a test-time optimization approach that simultaneously estimates scene depth and camera motion by fitting a neural RGB-D representation to long-burst data. The method uses a plane plus depth model, which is trained end-to-end and performs coarse-to-fine refinement by controlling which multi-resolution volume features the network has access to at what time during training. The results demonstrate geometrically accurate depth reconstructions with no additional hardware or separate data pre-processing and pose-estimation steps.