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

TL;DR

This paper introduces a new model for non-line-of-sight imaging that accounts for partial occlusions and surface normals, enabling more accurate scene reconstruction from time-resolved photon data.

Contribution

The authors develop a factored light transport model and a corresponding inverse reconstruction method that improves NLOS imaging accuracy in complex scenes.

Findings

High-fidelity reconstructions in simulation

Effective NLOS imaging with partial occlusions

Robustness to challenging scene configurations

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.