Flash Photography for Data-Driven Hidden Scene Recovery
Matthew Tancik, Guy Satat, Ramesh Raskar

TL;DR
This paper presents a novel method combining geometric and data-driven techniques to localize, identify, and reconstruct hidden objects around corners using consumer cameras and flash photography, achieving high accuracy and object recognition.
Contribution
It introduces a new approach that couples geometric understanding with data-driven models trained on rendered data for real-world hidden scene recovery.
Findings
Localized 12cm wide objects with 1.7cm accuracy
Identified object class with 87.7% accuracy
Demonstrated importance of beyond-corner scene area
Abstract
Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these tasks can be done around corners with consumer cameras. Recent techniques for NLOS imaging using consumer cameras have not been able to both localize and identify the hidden object. We introduce a method that couples traditional geometric understanding and data-driven techniques. To avoid the limitation of large dataset gathering, we train the data-driven models on rendered samples to computationally recover the hidden scene on real data. The method has three independent operating modes: 1) a regression output to localize a hidden object in 2D, 2) an identification output to identify the object type or pose, and 3) a generative network to reconstruct the hidden scene from a new viewpoint. The method is able to…
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Taxonomy
TopicsAdvanced Optical Sensing Technologies · Remote Sensing and LiDAR Applications · Advanced Vision and Imaging
