A practical means for calibrating an LED-based photometric stereo system

Xie, Limin; Song, Zhan; Jiao, Guohua; Huang, Xinhan; Jia, Kui

doi:10.1016/j.optlaseng.2014.07.006

Cited by 30 publications

(14 citation statements)

References 16 publications

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“…A large variety of solutions have been proposed, including assuming mechanically calibrated arrangements for domes and rings [RTF∗04], using known calibration objects [GCD∗18, Mac15, CCC08, MDA02, GCD∗17], and/or autocalibration techniques that reconstruct illumination parameters by making assumptions on the image reflectance field [WMTG05, XDW15, PF14, GCD∗18] or on the light form factor [ASSS14, USTY15, PCGG16, XSJ∗15]. Ackermann et al [AFG13] present a validation through error statistics of both a forward and backward geometric point light source calibration by using sets of different numbers of reflective spheres, showing that significant errors are present even in common use cases just for the identification of light position.…”

Section: Introductionmentioning

confidence: 99%

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

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Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.

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Section: Introductionmentioning

confidence: 99%

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

Computer Graphics Forum

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“…Near‐lighting photometric stereo PS estimates surface normals from two or more images with a fixed camera but differing lighting conditions [XSJ*]. Many PS methods assume that a light arrives from a distant source, which forms parallel light rays.…”

Section: Related Workmentioning

confidence: 99%

Normal‐Based Bas‐Relief Modelling via Near‐Lighting Photometric Stereo

Wei

Song

Nie

et al. 2019

Computer Graphics Forum

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We present a near‐lighting photometric stereo (NL‐PS) system to produce digital bas‐reliefs from a physical object (set) directly. Unlike both the 2D image and 3D model‐based modelling methods that require complicated interactions and transformations, the technique using NL‐PS is easy to use with cost‐effective hardware, providing users with a trade‐off between abstract and representation when creating bas‐reliefs. Our algorithm consists of two steps: normal map acquisition and constrained 3D reconstruction. First, we introduce a lighting model, named the quasi‐point lighting model (QPLM), and provide a two‐step calibration solution in our NL‐PS system to generate a dense normal map. Second, we filter the normal map into a detail layer and a structure layer, and formulate detail‐ or structure‐preserving bas‐relief modelling as a constrained surface reconstruction problem of solving a sparse linear system. The main contribution is a WYSIWYG (i.e. what you see is what you get) way of building new solvers that produces multi‐style bas‐reliefs with their geometric structures and/or details preserved. The performance of our approach is experimentally validated via comparisons with the state‐of‐the‐art methods.

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“…To our knowledge, there are few calibration procedures taking into account these phenomena. In [67], Xie et al use a single pattern, which is partially specular and partially Lambertian, to calibrate a LED. We intend to improve this procedure using two patterns, one specular and the other Lambertian.…”

Section: Calibrating the Luminous Flux Emitted By A Ledmentioning

confidence: 99%

“…This can also be achieved online, by introducing spheres in the scene to reconstruct [37]. Calibrating anisotropy is a more challenging problem, which was tackled recently in [48,67] by using images of a planar surface. Some photometric stereo methods also circumvent calibration by (partly or completely) automatically inferring lighting during the 3D-reconstruction process [36,37,38,44,51,57].…”

Section: Introductionmentioning

confidence: 99%

LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution

Quéau

Durix

et al. 2017

J Math Imaging Vis

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We conduct a thorough study of photometric stereo under nearby point light source illumination, from modeling to numerical solution, through calibration. In the classical formulation of photometric stereo, the luminous fluxes are assumed to be directional, which is very difficult to achieve in practice. Rather, we use light-emitting diodes (LEDs) to illuminate the scene to be reconstructed. Such point light sources are very convenient to use, yet they yield a more complex photometric stereo model which is arduous to solve. We first derive in a physically sound manner this model, and show how to calibrate its parameters. Then, we discuss two state-of-the-art numerical solutions. The first

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A practical means for calibrating an LED-based photometric stereo system

Cited by 30 publications

References 16 publications

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Normal‐Based Bas‐Relief Modelling via Near‐Lighting Photometric Stereo

LED-Based Photometric Stereo: Modeling, Calibration and Numerical Solution

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