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Found 7 papers, 0 papers with code
A Neural Height-Map Approach for the Binocular Photometric Stereo Problem
As in recent neural multi-view shape estimation frameworks such as NeRF, SIREN and inverse graphics approaches to multi-view photometric stereo (e. g. PS-NeRF) we formulate shape estimation task as learning of a differentiable surface and texture representation by minimising surface normal discrepancy for normals estimated from multiple varying light images for two views as well as discrepancy between rendered surface intensity and observed images.
A CNN Based Approach for the Point-Light Photometric Stereo Problem
Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered.
LUCES: A Dataset for Near-Field Point Light Source Photometric Stereo
In order to fill the gap in evaluating near-field photometric stereo methods, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of a varying of materials.
A CNN Based Approach for the Near-Field Photometric Stereo Problem
Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration.
PX-NET: Simple and Efficient Pixel-Wise Training of Photometric Stereo Networks
We show that global physical effects can be approximated on the observation map domain and this simplifies and speeds up the data creation procedure.
A Differential Volumetric Approach to Multi-View Photometric Stereo
In this work, we present a volumetric approach to the multi-view photometric stereo problem.
Semi-Calibrated Near Field Photometric Stereo
3D reconstruction from shading information through Photometric Stereo is considered a very challenging problem in Computer Vision.
