Non-line-of-sight imaging with partial occluders and surface normals

Felix Heide, Matthew O'Toole, Kai Zang, David B. Lindell, Steven Diamond, Gordon Wetzstein

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

Imaging objects obscured by occluders is a significant challenge for many applications. A camera that could “see around corners” could help improve navigation and mapping capabilities of autonomous vehicles or make search and rescue missions more effective. Time-resolved single-photon imaging systems have recently been demonstrated to record optical information of a scene that can lead to an estimation of the shape and reflectance of objects hidden from the line of sight of a camera. However, existing non-line-of-sight (NLOS) reconstruction algorithms have been constrained in the types of light transport effects they model for the hidden scene parts. We introduce a factored NLOS light transport representation that accounts for partial occlusions and surface normals. Based on this model, we develop a factorization approach for inverse time-resolved light transport and demonstrate high-fidelity NLOS reconstructions for challenging scenes both in simulation and with an experimental NLOS imaging system.
Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalACM Transactions on Graphics
Volume38
Issue number3
DOIs
StatePublished - May 9 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-10
Acknowledgements: D. B. L. is supported by a Stanford Graduate Fellowship in Science and Engineering. G. W. is supported by a Terman Faculty Fellowship and a Sloan Fellowship. Additional funding was generously provided by the National Science Foundation (CAREER Award IIS 1553333), the DARPA REVEAL program, the ARO (Grant W911NF-19-1-0120), and by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant. The authors would like to thank James Harris for fruitful discussions. D.B.L. is supported by a Stanford Graduate Fellowship in Science and Engineering. G.W. is supported by a Terman Faculty Fellowship and a Sloan Fellowship. Additional funding was generously provided by the National Science Foundation (CAREER Award IIS 1553333), the DARPA REVEAL program, the ARO (Grant W911NF-19-1-0120), and by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

Fingerprint

Dive into the research topics of 'Non-line-of-sight imaging with partial occluders and surface normals'. Together they form a unique fingerprint.

Cite this