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Found 10 papers, 3 papers with code
RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo.
A Variational Approach to Shape-from-shading Under Natural Illumination
A numerical solution to shape-from-shading under natural illumination is presented.
Variational Reflectance Estimation from Multi-view Images
We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry.
Normal Integration: A Survey
In the first part of this survey, we select the most important properties that one may expect from a normal integration method, based on a thorough study of two pioneering works by Horn and Brooks [28] and by Frankot and Chellappa [19].
Variational Methods for Normal Integration
The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry, etc.
LED-based Photometric Stereo: Modeling, Calibration and Numerical Solution
The second one directly recovers the depth, by formulating photometric stereo as a system of PDEs which are partially linearized using image ratios.
A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting
This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method.
Dense Multi-view 3D-reconstruction Without Dense Correspondences
We introduce a variational method for multi-view shape-from-shading under natural illumination.
Fast and Accurate Surface Normal Integration on Non-Rectangular Domains
The integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision.
Unbiased Photometric Stereo for Colored Surfaces: A Variational Approach
3D shape recovery using photometric stereo (PS) gained increasing attention in the computer vision community in the last three decades due to its ability to recover the thinnest geometric structures.
