TY - GEN
T1 - Efficient Non-Line-of-Sight Imaging from Transient Sinograms
AU - Isogawa, Mariko
AU - Chan, Dorian
AU - Yuan, Ye
AU - Kitani, Kris
AU - O’Toole, Matthew
N1 - Funding Information:
Acknowledgements. We thank Ioannis Gkioulekas for helpful discussions and feedback on this work. M. Isogawa is supported by NTT Corporation. M. O’Toole is supported by the DARPA REVEAL program.
Funding Information:
We thank Ioannis Gkioulekas for helpful discussions and feedback on this work. M. Isogawa is supported by NTT Corporation. M. O?Toole is supported by the DARPA REVEAL program.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e.g., walls) to see around corners. One approach involves using pulsed lasers and ultrafast sensors to measure the travel time of multiply scattered light. Unlike existing NLOS techniques that generally require densely raster scanning points across the entirety of a relay wall, we explore a more efficient form of NLOS scanning that reduces both acquisition times and computational requirements. We propose a circular and confocal non-line-of-sight (C 2NLOS) scan that involves illuminating and imaging a common point, and scanning this point in a circular path along a wall. We observe that (1) these C 2NLOS measurements consist of a superposition of sinusoids, which we refer to as a transient sinogram, (2) there exists computationally efficient reconstruction procedures that transform these sinusoidal measurements into 3D positions of hidden scatterers or NLOS images of hidden objects, and (3) despite operating on an order of magnitude fewer measurements than previous approaches, these C 2NLOS scans provide sufficient information about the hidden scene to solve these different NLOS imaging tasks. We show results from both simulated and real C 2NLOS scans (Project page: https://marikoisogawa.github.io/project/c2nlos).
AB - Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e.g., walls) to see around corners. One approach involves using pulsed lasers and ultrafast sensors to measure the travel time of multiply scattered light. Unlike existing NLOS techniques that generally require densely raster scanning points across the entirety of a relay wall, we explore a more efficient form of NLOS scanning that reduces both acquisition times and computational requirements. We propose a circular and confocal non-line-of-sight (C 2NLOS) scan that involves illuminating and imaging a common point, and scanning this point in a circular path along a wall. We observe that (1) these C 2NLOS measurements consist of a superposition of sinusoids, which we refer to as a transient sinogram, (2) there exists computationally efficient reconstruction procedures that transform these sinusoidal measurements into 3D positions of hidden scatterers or NLOS images of hidden objects, and (3) despite operating on an order of magnitude fewer measurements than previous approaches, these C 2NLOS scans provide sufficient information about the hidden scene to solve these different NLOS imaging tasks. We show results from both simulated and real C 2NLOS scans (Project page: https://marikoisogawa.github.io/project/c2nlos).
KW - Computational imaging
KW - Non-line-of-sight imaging
UR - http://www.scopus.com/inward/record.url?scp=85097363591&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097363591&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58571-6_12
DO - 10.1007/978-3-030-58571-6_12
M3 - Conference contribution
AN - SCOPUS:85097363591
SN - 9783030585709
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 193
EP - 208
BT - Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
A2 - Vedaldi, Andrea
A2 - Bischof, Horst
A2 - Brox, Thomas
A2 - Frahm, Jan-Michael
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th European Conference on Computer Vision, ECCV 2020
Y2 - 23 August 2020 through 28 August 2020
ER -