In this paper, we address the" dual problem" of multi-view scene reconstruction in which we
utilize single-view images captured under different point lights to learn a neural scene …

Reconstructing the 3D shape of an object using several images under different light sources
is a very challenging task, especially when realistic assumptions such as light propagation …

Photometric stereo recovers three-dimensional (3D) object surface normal from multiple
images under different illumination directions. Traditional photometric stereo methods suffer …

This paper presents an uncalibrated deep neural network framework for the photometric
stereo problem. For training models to solve the problem, existing neural network-based …

Retrieving accurate 3D reconstructions of objects from the way they reflect light is a very
challenging task in computer vision. Despite more than four decades since the definition of …

Three-dimensional reconstruction of objects from shading information is a challenging task
in computer vision. As most of the approaches facing the Photometric Stereo problem use …

We introduce the first end-to-end learning-based solution to near-field Photometric Stereo
(PS), where the light sources are close to the object of interest. This setup is especially …

Photometric stereo is a widely used optical 3-D reconstruction method, which has
advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture …

Monocular depth estimation in endoscopy videos can enable assistive and robotic surgery
to obtain better coverage of the organ and detection of various health issues. Despite …

This paper presents 'DeepShadow', a one-shot method for recovering the depth map and
surface normals from photometric stereo shadow maps. Previous works that try to recover …