General Specular Surface Triangulation

Bonfort, Thomas; Sturm, Peter; Gargallo, Pau

doi:10.1007/11612704_87

Cited by 57 publications

(67 citation statements)

References 15 publications

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“…It was difficult to evaluate the estimated pose, so we evaluate it indirectly as follows. In [1], we describe an approach for the reconstruction of general specular surfaces from two images of the reference object's reflections. Here, to perform a quantitative evaluation, we reconstruct a planar specular surface (a hard drive platter), without making use of the planarity information for the reconstruction.…”

Section: Surface Reconstructionmentioning

confidence: 99%

“…The LCD monitor is partly visible in the image, but only its reflections are used to compute camera pose. The specular surface is reconstructed as a dense point cloud [1], to which we fit a plane (linear least squares fitting without outlier removal). Over 98% of the roughly 525,000 computed points were less than 0.2 mm away from the computed plane and 64% less than 0.1 mm.…”

Section: Surface Reconstructionmentioning

confidence: 99%

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How to Compute the Pose of an Object Without a Direct View?

Sturm

Bonfort

2006

Computer Vision – ACCV 2006

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Abstract. We consider the task of computing the pose of an object relative to a camera, for the case where the camera has no direct view of the object. This problem was encountered in work on vision-based inspection of specular or shiny surfaces, that is often based on analyzing images of calibration grids or other objects, reflected in such a surface. A natural setup consists thus of a camera and a calibration grid, put side-by-side, i.e. without the camera having a direct view of the grid. A straightforward idea for computing the pose is to place planar mirrors such that the camera sees the calibration grid's reflection. In this paper, we consider this idea, describe geometrical properties of the setup and propose a practical algorithm for the pose computation.

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Section: Surface Reconstructionmentioning

confidence: 99%

Section: Surface Reconstructionmentioning

confidence: 99%

How to Compute the Pose of an Object Without a Direct View?

Sturm

Bonfort

2006

Computer Vision – ACCV 2006

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“…The "calibration process" or "geometrical calibration" of the setup aims to estimate this geometrical information. Actual publications deal with this problem through a simple experiment using a flat (usually < 1λ) mirror [3][4][5][6][7]. This experiment is repeated several times with different mirror tilts and positions.…”

Section: Calibration Of Measurement Setupmentioning

confidence: 99%

“…the recorded camera picture is compared with a simulated one using the estimated camera pose). The authors of this work calibrate their deflectometry setup according to the work of Bonfort et al [4] being one of the oldest (as far as the authors know) published implementation of the idea of "virtual" cameras.…”

Section: Calibration Of Measurement Setupmentioning

confidence: 99%

Quality control of Fresnel lens molds using deflectometry

Kiefel

Nitz

2016

AIP Conference Proceedings

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Abstract. Fresnel lenses are widely used in CPV applications and can be manufactured in mass replication processes like injection molding, embossing or crosslinking. The quality control of the designed function of refractive replica includes the assessment of surface errors and replication errors. Since deviations of the mold surface from design are transferred to the replica, the direct characterization of mold surfaces enables error detection early in the fabrication chain. We show the application of a developed deflectometry method to Fresnel shaped metallic molds. The method detects slope deviations from ideal surface slopes in sub-mrad resolution. The results can be easily included in ray tracing simulations to predict the potential optical function of a replica which was manufactured by the respective mold.

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“…A variety of techniques have been developed to detect and reconstruct these surfaces [1,4] using a variety of constraints, however in this work we attempt to reconstruct a reflecting surface and a reflected scene using different imaging modalities. Using a four camera system shown in Fig.…”

Section: Figure 1: the Four Camera Systemmentioning

confidence: 99%

Multimodal Stereo Vision For Reconstruction In The Presence Of Reflection

Sorensen

Saponaro

Rhein

et al. 2015

Procedings of the British Machine Vision Conference 2015

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Traditional stereo approaches assume a lambertian scene, an assumption which is violated in the presence of specular reflections. A variety of techniques have been developed to detect and reconstruct these surfaces [1, 4] using a variety of constraints, however in this work we attempt to reconstruct a reflecting surface and a reflected scene using different imaging modalities. Using a four camera system shown in Fig. 1, operating as two stereo pairs we reconstruct a reflecting surface as well as a reflected scene. The camera system consists of a pair of visible band cameras and a pair of Long Wave InfraRed (LWIR) cameras. We leverage these different imaging modalities by taking advantage of the fact that reflectivity is wavelength dependent and reflective materials in one modality can appear non reflective in complementing modalities.We calibrate the system by extending the work of [3] to handle cross modality calibration. This gives us a common coordinate system. To reconstruct a reflected scene we first extract the reflecting surface. This is done by adding texture to the surface in only one modality, and reconstructing correspondences, and fitting a plane to these points using principle component analysis. The implicit representation of this planecan be used to intersect rays for reconstruction. Where P is the origin, n is the plane normal and d is a constant. Reconstructing the scene is accomplished by stereo matching the reflected images using uncalibrated rectification and disparity matching using [2]. Each point in the disparity map can be viewed as a ray going from the camera center through the image plane defined by,where C 0 is the camera center, andwhere A is the camera matrix, and R is the camera rotation matrix, and [x i , y i ] is the pixel location. We intersect these rays with the reflecting surface by substituting P from equation 1 with V i from equation 2, and the reflected ray direction is defined as,

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General Specular Surface Triangulation

Cited by 57 publications

References 15 publications

How to Compute the Pose of an Object Without a Direct View?

How to Compute the Pose of an Object Without a Direct View?

Quality control of Fresnel lens molds using deflectometry

Multimodal Stereo Vision For Reconstruction In The Presence Of Reflection

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