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Found 17 papers, 6 papers with code
Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems trained with Gradient Descent
Advanced machine learning methods, and more prominently neural networks, have become standard to solve inverse problems over the last years.
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.
Convergence and Recovery Guarantees of Unsupervised Neural Networks for Inverse Problems
Neural networks have become a prominent approach to solve inverse problems in recent years.
Convergence Guarantees of Overparametrized Wide Deep Inverse Prior
Neural networks have become a prominent approach to solve inverse problems in recent years.
MS-PS: A Multi-Scale Network for Photometric Stereo With a New Comprehensive Training Dataset
The photometric stereo (PS) problem consists in reconstructing the 3D-surface of an object, thanks to a set of photographs taken under different lighting directions.
On the well-posedness of uncalibrated photometric stereo under general lighting
While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting.
Variational Uncalibrated Photometric Stereo under General Lighting
Photometric stereo (PS) techniques nowadays remain constrained to an ideal laboratory setup where modeling and calibration of lighting is amenable.
Photometric Depth Super-Resolution
This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to the higher resolution of the companion RGB image.
A Variational Approach to Shape-from-shading Under Natural Illumination
A numerical solution to shape-from-shading under natural illumination is presented.
Optimisation of photometric stereo methods by non-convex variational minimisation
Estimating shape and appearance of a three dimensional object from a given set of images is a classic research topic that is still actively pursued.
Variational Reflectance Estimation from Multi-view Images
We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry.
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.
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].
Depth Super-Resolution Meets Uncalibrated Photometric Stereo
A novel depth super-resolution approach for RGB-D sensors is presented.
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.
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.
