A variational analysis of shape from specularities using sparse data

Solem, Jan Erik; Heyden, Anders; Aanæs, Henrik

doi:10.1109/tdpvt.2004.1335137

Cited by 27 publications

(25 citation statements)

References 24 publications

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“…When motion is incorporated (e.g., by changing the viewpoint or moving the object or the light source) under calibrated illumination, one can track the isolated specular highlights to exploit constrains between the surface normal, viewing direction, and the illumination direction [7,23,25]. This facilitates shape reconstruction in sparse locations only, a limitation which was initially addressed by extended motion sequences, object modeling, or deep regularizations (e.g., [12,14,19]). …”

Section: Previous Workmentioning

confidence: 99%

Specular Flow and Shape in One Shot:

Adato

Ben-Shahar

2011

Procedings of the British Machine Vision Conference 2011

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The reconstruction of specular shape from images is a very challenging task, especially when the illumination environment is unknown. In such cases, the problem becomes more tractable once the camera observes relative motion between the object and the environment, which induces a type of optical flow field that has become known as specular flow. Unfortunately, however, the estimation of specular flow from image sequences is even more challenging, putting in question the effectiveness of the shape from specular flow approach as a whole. Here we show that instead of the traditional (and somewhat futile) process of first estimating the specular flow and then using it for the recovery of specular shape, an approach that addresses these two inference problems simultaneously improves the estimation of both structures. We formulate the problem in a variational setting, we identify and address numerical issues unique to its application on specular flows, and we employ a polar representation of motion, all to result in the first ever practical method to compute specular shape from real image sequences under unknown illumination.

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Section: Previous Workmentioning

confidence: 99%

Specular Flow and Shape in One Shot:

Adato

Ben-Shahar

2011

Procedings of the British Machine Vision Conference 2011

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“…Traditionally (cf. [SAH04,KD04]), one would blend both functionals into a third one, e.g. E(S) = αJ(S) + (1 − α)H(S), and then minimize E. We will adopt a different viewpoint inspired by [SO05a]: let R be the set of regular surfaces (or even level set surfaces if necessary), and denote by Rm ⊂ R the subset of all surfaces satisfying the normal condition, i.e.…”

Section: Stating the Problemmentioning

confidence: 99%

“…Solem et al investigate the problem in a variational setting, cf. [SAH04]. Assuming a set of surface points is known, they propose an iterative algorithm to minimize an energy functional consisting of surface normal and point constraints.…”

Section: Introduction and Previous Workmentioning

confidence: 99%

Shape from Specular Reflection and Optical Flow

Lellmann

Balzer

Rieder³

et al. 2008

Int J Comput Vis

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Inferring scene geometry from a sequence of camera images is one of the central problems in computer vision. While the overwhelming majority of related research focuses on diffuse surface models, there are cases when this is not a viable assumption: in many industrial applications, one has to deal with metal or coated surfaces exhibiting a strong specular behavior. We propose a novel and generalized constrained gradient descent method to determine the shape of a purely specular object from the reflection of a calibrated scene and additional data required to find a unique solution. This data is exemplarily provided by optical flow measurements obtained by small scale motion of the specular object. We present a general forward model to predict the optical flow of specular surfaces, covering rigid body motion as well as elastic deformation, and allowing a characterization of problematic points. We demonstrate the applicability of our method by numerical experiments.

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“…The approach was extended by Tarini et al [7] who integrate around a seed point, and use a global self-coherence measure to estimate the correct depth for the seed point. Under a distant light configuration, Solem et al [8] fit a level-set surface with a variational approach.…”

Section: Previous Workmentioning

confidence: 99%

General Specular Surface Triangulation

Bonfort

Sturm

Gargallo

2006

Computer Vision – ACCV 2006

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Abstract. We present a method for the reconstruction of a specular surface, using a single camera viewpoint and the reflection of a planar target placed at two different positions. Contrarily to most specular surface reconstruction algorithms, our method makes no assumption on the regularity or continuity of the specular surface, and outputs a set of 3D points along with corresponding surface normals, all independent from one another. A point on the specular surface can be reconstructed if its corresponding pixel in the image has been matched to its source in both of the target planes. We present original solutions to the problem of dense point matching and planar target pose estimation, along with reconstruction results in real-world scenarii.

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A variational analysis of shape from specularities using sparse data

Abstract: Looking around in our every day environment, many of the encountered objects are specular to some degree.

Cited by 27 publications

References 24 publications

Specular Flow and Shape in One Shot:

Specular Flow and Shape in One Shot:

Shape from Specular Reflection and Optical Flow

General Specular Surface Triangulation

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