What You Can Learn by Staring at a Blank Wall

ICCV 2021 (Oral)

Prafull Sharma, Miika Aittala, Yoav Y. Schechner, Antonio Torralba,
Gregory W. Wornell, William T. Freeman, Fredo Durand

Paper Supplementary Code (coming soon!)

Abstract

We present a passive non-line-of-sight method that infers the number of people or activity of a person from the observation of a blank wall in an unknown room. Our technique analyzes complex imperceptible changes in indirect illumination in a video of the wall to reveal a signal that is correlated with motion in the hidden part of a scene. We use this signal to classify between zero, one, or two moving people, or the activity of a person in the hidden scene. We train two convolutional neural networks using data collected from 20 different scenes, and achieve an accuracy of ≈94% for both tasks in unseen test environments and real-time online settings. Unlike other passive non-line-of-sight methods, the technique does not rely on known occluders or controllable light sources, and generalizes to unknown rooms with no recalibration. We analyze the generalization and robustness of our method with both real and synthetic data, and study the effect of the scene parameters on the signal quality.


Bibtex


@InProceedings{Sharma_2021_ICCV, author = {Sharma, Prafull and Aittala, Miika and Schechner, Yoav Y. and Torralba, Antonio and Wornell, Gregory W. and Freeman, William T. and Durand, Fredo}, title = {What You Can Learn by Staring at a Blank Wall}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {2330-2339} }