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 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 …

In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free,
and Universal Photometric Stereo network. Our approach can recover astonishingly intricate …

This paper tackles the task of uncalibrated photometric stereo for 3D object reconstruction,
where both the object shape, object reflectance, and lighting directions are unknown. This is …

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 …

We propose a fast and generalizable solution to Multiview Photometric Stereo (MVPS),
called MVPSNet. The key to our approach is a feature extraction network that effectively …

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 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 …