2013
DOI: 10.1007/s00138-013-0517-x
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Structured light self-calibration with vanishing points

Abstract: This paper introduces the vanishing points to self-calibrate a structured light system. The vanishing points permit to automatically remove the projector's keystone effect and then to self-calibrate the projector-camera system. The calibration object is a simple planar surface such as a white paper. Complex patterns and 3D calibrated objects are not required any more. The technique is compared to classic calibration and validated with experimental results.

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Cited by 27 publications
(14 citation statements)
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References 27 publications
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“…The solution of the unknowns (dz j , dz i , dx j and dx i ) is depicted in Equation (4), which is known as the normal equations. Finally, the X and Z compensated coordinates of CPs are calculated as shown in (5) and (6). Note that the Y coordinates are not compensated in this procedure, as the height h is directly measured on the screen surface, and thus is acquired with more accuracy.…”
Section: Control Pointmentioning
confidence: 99%
See 1 more Smart Citation
“…The solution of the unknowns (dz j , dz i , dx j and dx i ) is depicted in Equation (4), which is known as the normal equations. Finally, the X and Z compensated coordinates of CPs are calculated as shown in (5) and (6). Note that the Y coordinates are not compensated in this procedure, as the height h is directly measured on the screen surface, and thus is acquired with more accuracy.…”
Section: Control Pointmentioning
confidence: 99%
“…Many works on projector or multi-projector displays are based on planar surfaces [2][3][4][5][6][7][8], for which automated geometric correction and alignment is simplified through the use of planar homographies between the planar screen, the projector frame buffers, and the images of one or more cameras observing the screen. Many of these developments use chessboards-based planar references alone or with a combination of other planar surfaces to automatically measure 2D points (i.e., image points) and establish point correspondences [8][9][10][11][12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Nowadays, several methods exist in 3D modelling: i) image based methods that exploit photogrammetric aspects in creating high fidelity 3D maps (Remondino and Zhang, 2006;Hullo et al, 2009;Haala and Rothermel, 2012), ii) photometric stereo that exploits light reflection properties for 3D modelling (Argyriou et al, 2014), iii) real-time depth sensors, such as Kinect, to create cost-effective but of low fidelity RGBD images (Izadi, 2011), iv) structured light technologies with the capability of simultaneously capture 3D geometry and texture (Soile et al, 2013;Orghidan, 2014) and v) laser scanning for large scale automated 3D reconstruction (Valanis et al, 2009;Hai, 2013). However, each of the aforementioned methods present advantages/disadvantages making high fidelity 3D modelling of CH assets a complex decision making process.…”
Section: Previous Workmentioning
confidence: 99%
“…The extrinsic parameters are related to the geometric structure of the FPP, e.g., the locations of the camera and the projector with respect to the object to be measured, and the intrinsic parameters are from the camera and projector structure. These parameters can be determined through camera calibration (CC) [6][7][8][9][10][11][12] and projector calibration (PC) [13][14][15][16][17][18][19][20][21][22]. When a camera is calibrated, the corresponding relationship can be established between the three-dimensional (3D) shape of an object and the corresponding two-dimensional (2D) image taken by the camera (referred to as the camera image).…”
Section: Introductionmentioning
confidence: 99%
“…1. The projector can be calibrated using two different approaches [13][14][15][16][17][18][19][20][21][22].…”
Section: Introductionmentioning
confidence: 99%