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[HTML][HTML] Deep learning in optical metrology: a review
With the advances in scientific foundations and technological implementations, optical
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
metrology has become versatile problem-solving backbones in manufacturing, fundamental …
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 …
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 …
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 …
Shape from polarization for complex scenes in the wild
We present a new data-driven approach with physics-based priors to scene-level normal
estimation from a single polarization image. Existing shape from polarization (SfP) works …
estimation from a single polarization image. Existing shape from polarization (SfP) works …
DiLiGenRT: A photometric stereo dataset with quantified roughness and translucency
Photometric stereo faces challenges from non-Lambertian reflectance in real-world
scenarios. Systematically measuring the reliability of photometric stereo methods in …
scenarios. Systematically measuring the reliability of photometric stereo methods in …
S-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
In this paper, we address the" dual problem" of multi-view scene reconstruction in which we
utilize single-view images captured under different point lights to learn a neural scene …
utilize single-view images captured under different point lights to learn a neural scene …
Deep photometric stereo for non-lambertian surfaces
This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated
scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully …
scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully …
Deep 3d capture: Geometry and reflectance from sparse multi-view images
We introduce a novel learning-based method to reconstruct the high-quality geometry and
complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images …
complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images …