Jean-Denis Durou — accepted AI-conference papers

2024

RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction

Baptiste Brument, Robin Bruneau, Yvain Quéau, Jean Mélou, François Bernard Lauze, Jean-Denis Durou +1

CVPR 2024poster

This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal considering them as a vector of radiances rendered under simulated v…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2017

A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting

Yvain Queau, Tao Wu, Francois Lauze, Jean-Denis Durou, Daniel Cremers

CVPR 2017poster

This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-sh…

Cited by 73 PDF ↗Scholar ↗

2016

Unbiased Photometric Stereo for Colored Surfaces: A Variational Approach

Yvain Queau, Roberto Mecca, Jean-Denis Durou

CVPR 2016poster

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. Yet, the reliabiliy of PS for color images is difficult to guarantee, because existing methods are u…

Cited by 52 PDF ↗Scholar ↗