Non-line-of-sight Imaging with Partial Occluders and Surface Normals
This addresses the challenge of seeing around corners for applications like autonomous vehicles and search and rescue, representing an incremental improvement over existing NLOS methods.
The paper tackled the problem of imaging objects hidden from direct view by introducing a factored non-line-of-sight (NLOS) light transport model that accounts for partial occlusions and surface normals, enabling high-fidelity reconstructions in challenging scenes through simulation and experiments.
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.