Non line of Sight Imaging via Neural Transient Fields
We present a neural modeling framework for non line of sight (NLOS) imaging. Previous solutions have sought to explicitly recover the 3D geometry (e.g. as point clouds) or voxel density (e.g. within a pre defined volume) of the hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF) approach, we use a multi layer perceptron (MLP) to represent the neural transient field or NeTF. However, NeTF measures the transient over spherical wavefronts rather than the radiance along lines. We therefore formulate a spherical volume NeTF reconstruction pipeline, applicable to both confocal and non confocal setups. Compared with NeRF, NeTF samples a much sparser set of viewpoints (scanning spots) and the sampling is highly uneven. We thus introduce a Monte Carlo technique to improve the robustness in the reconstruction. Experiments on synthetic and real datasets demonstrate NeTF achieves state of the art performance and can provide reliable reconstructions even under semi occlusions and on non Lambertian materials.
IEEE Transactions on Pattern Analysis and Machine Intelligence