Non-line-of-sight Imaging with Partial Occluders and Surface Normals

被引:110
作者
Heide, Felix [1 ,3 ]
O'Toole, Matthew [1 ,2 ,4 ]
Zang, Kai [1 ,5 ]
Lindell, David [1 ,6 ]
Diamond, Steven [1 ,6 ]
Wetzstein, Gordon [1 ,6 ]
机构
[1] Stanford Univ, Stanford, CA 94305 USA
[2] CMU, Pittsburgh, PA USA
[3] 35 Olden St, Princeton, NJ 08540 USA
[4] 4800 Forbes Ave, Pittsburgh, PA 15213 USA
[5] 1265 Lakeside Dr 3174, Sunnyvale, CA 94085 USA
[6] 350 Serra Mall, Stanford, CA 94305 USA
来源
ACM TRANSACTIONS ON GRAPHICS | 2019年 / 38卷 / 03期
基金
美国国家科学基金会;
关键词
Computational photography; non-line-of-sight imaging;
D O I
10.1145/3269977
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
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.
引用
收藏
页数:10
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