  Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in
recent years, due to its ability to image objects that are out of sight. The
light transport condition plays an important role in this task since changing
the conditions will lead to different imaging models. Existing learning-based
NLOS methods usually train independent models for different light transport
conditions, which is computationally inefficient and impairs the practicality
of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging
method that effectively handles multiple light transport conditions with a
single network. We achieve this by inferring a latent light transport
representation from the projection image and using this representation to
modulate the network that reconstructs the hidden image from the projection
image. We train a light transport encoder together with a vector quantizer to
obtain the light transport representation. To further regulate this
representation, we jointly learn both the reconstruction network and the
reprojection network during training. A set of light transport modulation
blocks is used to modulate the two jointly trained networks in a multi-scale
way. Extensive experiments on a large-scale passive NLOS dataset demonstrate
the superiority of the proposed method. The code is available at
https://github.com/JerryOctopus/NLOS-LTM.
