Specularities Reduce Ambiguity of Uncalibrated Photometric Stereo

Drbohlav, Ondřej; Šára, Radim

doi:10.1007/3-540-47967-8_4

Cited by 53 publications

(43 citation statements)

References 12 publications

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“…Drbohlav and Šára [11,12] used multiple linearly polarized light source imaging to overcome ambiguities and determine shape. Atkinson and Hancock [5] applied multiple highly controlled but unpolarized illumination sources.…”

Section: Shape Analysismentioning

confidence: 99%

High-sensitivity analysis of polarization by surface reflection

Atkinson

Ernst

2018

Machine Vision and Applications

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The exploitation of polarization information in the field of computer vision has become progressively more popular during the last few decades. This is primarily due to (1) the fact that polarization is a source of mostly untapped information for machine vision; (2) the relative computational ease by which geometrical information about a scene (e.g. surface normals) may be extracted from polarization data; and (3) the recent introduction of camera hardware able to capture polarization data in real time. The motivation for this paper is that a detailed quantitative study into the precision of polarization measurements with respect to expectation has yet to be performed. The paper therefore presents a detailed analysis and optimization of the key aspects of data capture necessary to acquire the most precise (as opposed to fast) results for the benefit of future research into the field of "polarization vision." The paper mainly focuses on a rotating polarizer method as this is shown to be the most accurate for high-sensitivity measurements. Commercial polarization cameras by contrast generally sacrifice precision for the benefit of much shorter capture times. That said, the paper reviews the state of the art in polarization camera technology and quantitatively evaluates the performance of one such camera: the Fraunhofer "POLKA."

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Section: Shape Analysismentioning

confidence: 99%

High-sensitivity analysis of polarization by surface reflection

Atkinson

Ernst

2018

Machine Vision and Applications

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“…When the camera and source positions are known, for example, the surface normal and the surface depth at a specular point are both determined up to a one-parameter family. Constraints of this type have been used computationally for different tasks, including recognition (e.g., [4]) and surface reconstruction (e.g., [5], [6], [7]). In a similar vein, complex illumination environments that are known and controlled have been used to obtain higher order surface information (e.g., curvature) [8], [9], [10], [11], [12], [13], [14], [15] and to extract shape from multiple specular bounces [16].…”

Section: Related Workmentioning

confidence: 99%

Shape from Specular Flow

Adato

Vasilyev

Zickler

et al. 2010

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-An image of a specular (mirror-like) object is nothing but a distorted reflection of its environment. When the environment is unknown, reconstructing shape from such an image can be very difficult. This reconstruction task can be made tractable when, instead of a single image, one observes relative motion between the specular object and its environment, and therefore, a motion field-or specular flow-in the image plane. In this paper, we study the shape from specular flow problem and show that observable specular flow is directly related to surface shape through a nonlinear partial differential equation. This equation has the key property of depending only on the relative motion of the environment while being independent of its content. We take first steps toward understanding and exploiting this PDE, and we examine its qualitative properties in relation to shape geometry. We analyze several cases in which the surface shape can be recovered in closed form, and we show that, under certain conditions, specular shape can be reconstructed when both the relative motion and the content of the environment are unknown. We discuss numerical issues related to the proposed reconstruction algorithms, and we validate our findings using both real and synthetic data.

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“…Constraints of this type have been used computationally for different tasks, including recognition (e.g., [16]) and surface reconstruction (e.g. [8,7,20]). When two (stereo) views of a specular highlight are available and illumination consists of a known point-source, the apparent shift of the specular highlight relative to the surface constrains the surface curvature [2,3].…”

Section: Related Workmentioning

confidence: 99%