Shape from Specular Reflection and Optical Flow

Lellmann, Jan; Balzer, Jonathan; Rieder, Andreas; Beyerer, Jürgen

doi:10.1007/s11263-007-0123-3

Cited by 29 publications

(23 citation statements)

References 24 publications

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“…The novelty of our work lies in the fact that we do not require a specifically designed calibration target like a checkerboard [7] or a pattern on a calibrated monitor [8,24,14,2,15]. To our knowledge, our paper is the first to present practical results on outdoor reconstruction of specular surfaces.…”

Section: Previous Work and Contributionmentioning

confidence: 99%

Real-World Normal Map Capture for Nearly Flat Reflective Surfaces

Jacquet

Häne

Köser

et al. 2013

2013 IEEE International Conference on Computer Vision

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Section: Previous Work and Contributionmentioning

confidence: 99%

Real-World Normal Map Capture for Nearly Flat Reflective Surfaces

Jacquet

Häne

Köser

et al. 2013

2013 IEEE International Conference on Computer Vision

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“…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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“…1(a), largely deviate from their true color information in the specular regions. There are also work directly based on the specular component, such as shape from specular reflection [1]. Therefore, separating highlight from diffuse component for the images of non-Lambertian scenes is of crucial importance.…”

Section: Introductionmentioning

confidence: 99%

Fast and High Quality Highlight Removal From a Single Image

Suo

et al. 2016

IEEE Trans. on Image Process.

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Abstract-Specular reflection exists widely in photography and causes the recorded color deviating from its true value, so fast and high quality highlight removal from a single nature image is of great importance. In spite of the progress in the past decades in highlight removal, achieving wide applicability to the large diversity of nature scenes is quite challenging. To handle this problem, we propose an analytic solution to highlight removal based on an L2 chromaticity definition and corresponding dichromatic model. Specifically, this paper derives a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively. In the former illumination orthogonal subspace, which is specular-free, we can conduct robust clustering with an explicit criterion to determine the cluster number adaptively. In the latter illumination parallel subspace, a property called pure diffuse pixels distribution rule (PDDR) helps map each specular-influenced pixel to its diffuse component. In terms of efficiency, the proposed approach involves few complex calculation, and thus can remove highlight from high resolution images fast. Experiments show that this method is of superior performance in various challenging cases.Index Terms-Specular reflection, highlight removal, specular and diffuse, L2 normalized dichromatic model, adaptive material clustering.

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Shape from Specular Reflection and Optical Flow

Cited by 29 publications

References 24 publications

Real-World Normal Map Capture for Nearly Flat Reflective Surfaces

Real-World Normal Map Capture for Nearly Flat Reflective Surfaces

Shape from Specular Flow

Fast and High Quality Highlight Removal From a Single Image

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