A Theory of Fermat Paths for Non Line Of Sight Shape Reconstruction
We present a novel theory of Fermat paths of light between a known visible scene and an unknown object not in the line of sight of a transient camera. These light paths either obey specular reflection or are reflected by the object's boundary, and hence encode the shape of the hidden object. We prove that Fermat paths correspond to discontinuities in the transient measurements. We then derive a novel constraint that relates the spatial derivatives of the path lengths at these discontinuities to the surface normal. Based on this theory, we present an algorithm, called Fermat Flow, to estimate the shape of the non line of sight object. Our method allows, for the first time, accurate shape recovery of complex objects, ranging from diffuse to specular, that are hidden around the corner as well as hidden behind a diffuser. Finally, our approach is agnostic to the particular technology used for transient imaging. As such, we demonstrate mm scale shape recovery from pico second scale transients using a SPAD and ultrafast laser, as well as micron scale reconstruction from femto second scale transients using interferometry. We believe our work is a significant advance over the state of the art in non line of sight imaging.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)