Adaptive lighting for data-driven non-line-of-sight 3D localization and object identification

Sreenithy Chandran, Suren Jayasuriya

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations


Non-line-of-sight (NLOS) imaging of objects not visible to either the camera or illumination source is a challenging task with vital applications including surveillance and robotics. Recent NLOS reconstruction advances have been achieved using time-resolved measurements which requires expensive and specialized detectors and laser sources. In contrast, we propose a data-driven approach for NLOS 3D localization and object identification requiring only a conventional camera and projector. To generalize to complex line-of-sight (LOS) scenes with non-planar surfaces and occlusions, we introduce an adaptive lighting algorithm. This algorithm, based on radiosity, identifies and illuminates scene patches in the LOS which most contribute to the NLOS light paths, and can factor in system power constraints. We achieve an average identification of 87.1% object identification for four classes of objects, and average localization of the NLOS object's centroid with a mean-squared error (MSE) of 1.97 cm in the occluded region for real data taken from a hardware prototype. These results demonstrate the advantage of combining the physics of light transport with active illumination for data-driven NLOS imaging.

Original languageEnglish (US)
StatePublished - 2020
Event30th British Machine Vision Conference, BMVC 2019 - Cardiff, United Kingdom
Duration: Sep 9 2019Sep 12 2019


Conference30th British Machine Vision Conference, BMVC 2019
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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