Calibration of a three-dimensional reconstruction system using a structured light source

Marzani, Franck; Voisin, Yvon; Voon, Lew F.C. Lew Yan; Diou, Alain

doi:10.1117/1.1427673

Cited by 19 publications

(3 citation statements)

References 9 publications

(7 reference statements)

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“…In the field of one-shot techniques, an alternative to phase patterns is represented by patterns consisting of dot arrays [ 52 , 53 , 54 , 55 , 56 ], unidirectional lines [ 57 , 58 , 59 ], or crossed lines [ 60 , 61 , 62 ]. Edge detection or segmentation algorithms are required to identify the deformed patterns captured by the cameras.…”

Section: 3d Scanning Technologiesmentioning

confidence: 99%

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

Paoli

Neri

Razionale

et al. 2020

Sensors

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3D digital models of the upper limb anatomy represent the starting point for the design process of bespoke devices, such as orthoses and prostheses, which can be modeled on the actual patient’s anatomy by using CAD (Computer Aided Design) tools. The ongoing research on optical scanning methodologies has allowed the development of technologies that allow the surface reconstruction of the upper limb anatomy through procedures characterized by minimum discomfort for the patient. However, the 3D optical scanning of upper limbs is a complex task that requires solving problematic aspects, such as the difficulty of keeping the hand in a stable position and the presence of artefacts due to involuntary movements. Scientific literature, indeed, investigated different approaches in this regard by either integrating commercial devices, to create customized sensor architectures, or by developing innovative 3D acquisition techniques. The present work is aimed at presenting an overview of the state of the art of optical technologies and sensor architectures for the surface acquisition of upper limb anatomies. The review analyzes the working principles at the basis of existing devices and proposes a categorization of the approaches based on handling, pre/post-processing effort, and potentialities in real-time scanning. An in-depth analysis of strengths and weaknesses of the approaches proposed by the research community is also provided to give valuable support in selecting the most appropriate solution for the specific application to be addressed.

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Section: 3d Scanning Technologiesmentioning

confidence: 99%

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

Paoli

Neri

Razionale

et al. 2020

Sensors

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“…Calibration is performed by projecting pattern light onto a calibration object with 3-D geometry. Examples include noncoplanar lines, 10,11 a motorized mobile plane, 12 some known world planes, 13 something with a 3-D movable platform, 14 and 3-D calibration targets with known geometry. 15 2.…”

Section: Observationsmentioning

confidence: 99%

Projector calibration from the camera image point of view

Zhang

Zhu

2009

Opt. Eng

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The traditional projector calibration always assumes that the reprojection errors on the projector image are independent and identically distributed and utilize the same method as the camera calibration. Actually, even if the measured points on the camera image are independent and identically distributed Gaussian variables, it is impossible to obtain the statistical characteristics of the reprojection errors on the projector image, because the relation between the original measurement error and the reprojection error is nonlinear. We propose a method to estimate the projector parameters according to the camera image reprojection error. It does not require us to know the statistical characteristics of the projector image point and makes full use of the background knowledge of the camera image noise, because our cost function does not concern the reprojection error on the projector image as is the case in the traditional method. The simulations and experiments affirm that this method has higher precision, even if the real noise is not normal.

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“…The bright spot (about 4 2mm on a flat surface) is then imaged by the camera and used to estimate the object's depth by triangulation from the structured light source. 5 The object to be digitized is placed on a translation table that offers accurate horizontal and vertical displacements, with an error of 2% or less for a 1 m motion. Thus, the 2D coordinates X and Y are defined by translation of the table, while the depth coordinate Z can estimated by the triangulation scheme.…”

mentioning

confidence: 99%