Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …

Estimating depth from RGB images is a long-standing ill-posed problem, which has been
explored for decades by the computer vision, graphics, and machine learning communities …

Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple
disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural …

Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …

Photometric stereo aims to recover the surface normals of a 3D object from various shading
cues, establishing the relationship between two-dimensional images and the object …

Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it
provides more detailed surface textures and clearer structures. Photometric stereo can …

In this paper, we propose a novel method, namely GR-PSN, which learns surface normals
from photometric stereo images and generates the photometric images under distant …

Photometric stereo faces challenges from non-Lambertian reflectance in real-world
scenarios. Systematically measuring the reliability of photometric stereo methods in …

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 …

This paper aims at recovering the shape of a scene with unknown, non-Lambertian, and
possibly spatially-varying surface materials. When the shape of the object is highly complex …