15 research outputs found
Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
We recover a video of the motion taking place in a hidden scene by observing
changes in indirect illumination in a nearby uncalibrated visible region. We
solve this problem by factoring the observed video into a matrix product
between the unknown hidden scene video and an unknown light transport matrix.
This task is extremely ill-posed, as any non-negative factorization will
satisfy the data. Inspired by recent work on the Deep Image Prior, we
parameterize the factor matrices using randomly initialized convolutional
neural networks trained in a one-off manner, and show that this results in
decompositions that reflect the true motion in the hidden scene.Comment: 14 pages, 5 figures, Advances in Neural Information Processing
Systems 201