  Modern mobile burst photography pipelines capture and merge a short sequence
of frames to recover an enhanced image, but often disregard the 3D nature of
the scene they capture, treating pixel motion between images as a 2D
aggregation problem. We show that in a ''long-burst'', forty-two 12-megapixel
RAW frames captured in a two-second sequence, there is enough parallax
information from natural hand tremor alone to recover high-quality scene depth.
To this end, we devise a test-time optimization approach that fits a neural
RGB-D representation to long-burst data and simultaneously estimates scene
depth and camera motion. Our plane plus depth model 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. We validate
the method experimentally, and demonstrate geometrically accurate depth
reconstructions with no additional hardware or separate data pre-processing and
pose-estimation steps.
