  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. Comprehensive experiments on synthetic and real datasets
demonstrate NeTF provides higher quality reconstruction and preserves fine
details largely missing in the state-of-the-art.
