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Deep learning methods for calibrated photometric stereo and beyond
Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …
varying shading cues, ie, modeling the relationship between surface orientation and …
Ps-nerf: Neural inverse rendering for multi-view photometric stereo
Traditional multi-view photometric stereo (MVPS) methods are often composed of multiple
disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural …
disjoint stages, resulting in noticeable accumulated errors. In this paper, we present a neural …
A critical analysis of NeRF-based 3D reconstruction
This paper presents a critical analysis of image-based 3D reconstruction using neural
radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional …
radiance fields (NeRFs), with a focus on quantitative comparisons with respect to traditional …
Scalable, detailed and mask-free universal photometric stereo
In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free,
and Universal Photometric Stereo network. Our approach can recover astonishingly intricate …
and Universal Photometric Stereo network. Our approach can recover astonishingly intricate …
Deep DIC: Deep learning-based digital image correlation for end-to-end displacement and strain measurement
Digital image correlation (DIC) has become an industry standard to retrieve accurate
displacement and strain measurement in tensile testing and other material characterization …
displacement and strain measurement in tensile testing and other material characterization …
Deep Learning Methods for Calibrated Photometric Stereo and Beyond
Photometric stereo recovers the surface normals of an object from multiple images with
varying shading cues, ie, modeling the relationship between surface orientation and …
varying shading cues, ie, modeling the relationship between surface orientation and …
Normattention-psn: A high-frequency region enhanced photometric stereo network with normalized attention
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 …
cues, establishing the relationship between two-dimensional images and the object …
Self-calibrating deep photometric stereo networks
This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes
based on deep learning. Unlike previous approaches that heavily rely on assumptions of …
based on deep learning. Unlike previous approaches that heavily rely on assumptions of …
Estimating high-resolution surface normals via low-resolution photometric stereo images
Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it
provides more detailed surface textures and clearer structures. Photometric stereo can …
provides more detailed surface textures and clearer structures. Photometric stereo can …
GR-PSN: Learning to estimate surface normal and reconstruct photometric stereo images
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 …
from photometric stereo images and generates the photometric images under distant …