Inverse Polarization Raytracing: Estimating Surface Shapes of Transparent Objects

Miyazaki, Daisuke; Ikeuchi, Katsushi

doi:10.1109/cvpr.2005.195

Cited by 56 publications

(36 citation statements)

References 29 publications

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“…These algorithms apply exclusively to opaque surfaces that scatter incident light, and cannot handle scenes that contain transparent or highly-reflective media. For such scenes, the state of the art in reconstruction [5][6][7][8][9][10][11] is still confined to the simplest possible case-a surface bounding a single, homogeneous, transparent volume with no internal structures and no occlusion-and even this case cannot be solved without further assumptions (e.g., partially-known geometry [6,7], a volume that causes no more than two refractions [8], or ability to immerse in a refractive-index-matched liquid [9]). Unfortunately, while objects with transparent media are very common (Figure 1), they rarely appear in isolation and rarely have a simple enough shape to fall within the realm of existing techniques.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography

Morris

Kutulakos

2007

2007 IEEE 11th International Conference on Computer Vision

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We present a new method for reconstructing the exterior surface of a complex transparent scene with inhomogeneous interior (e.g., multiple interfaces, reflective or painted interiors, etc). Our approach involves capturing images of the scene from one or more viewpoints while moving a proximal light source to a 2D or 3D set of positions. This gives a 2D (or 3D) dataset per pixel, called the scatter trace. The key idea of our approach is that even though light transport within a transparent scene's interior can be exceedingly complex, the scatter trace of each pixel has a highlyconstrained geometry that (1) reveals the contribution of direct surface reflection, and (2) leads to a simple "scattertrace stereo" algorithm for computing the local geometry of the exterior surface (depth and surface normals). We present 3D reconstruction results for a variety of scenes that exhibit complex light transport phenomena.

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Section: Introductionmentioning

confidence: 99%

Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography

Morris

Kutulakos

2007

2007 IEEE 11th International Conference on Computer Vision

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“…This method is presented in Section 4. Third method 33) Solving the inverse problem of the polarization raytracing method, the shape of transparent objects can be estimated more precisely. The polarization raytracing method considers internal interreflection.…”

Section: Second Methodsmentioning

confidence: 99%

Shape Estimation of Transparent Objects by Using Polarization Analyses

Miyazaki

Ikeuchi

2006

ipsjdc

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Recently, techniques developed in the field of computer graphics and virtual reality have been applied to many environments, with the result that measuring the 3D shapes of real objects has become increasingly important. However, few methods have been proposed to measure the 3D shape of transparent objects such as glass and acrylics. In this paper, we introduce three methods that estimate the surface shape of transparent objects by using polarization analysis. The first method determines the surface shape of a transparent object by using knowledge established in the research field of thermodynamics. The second method determines the surface shape of a transparent object by using knowledge established in the research field of differential geometry. The third method gives an initial value of the surface shape and then determines the true surface shape of a transparent object by iterative computation. At the end of the paper, we discuss the advantages and the disadvantages of these three methods.

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“…Optical flow can be formulated to account for refraction [1,30] or reflection [8]. Miyazaki and Ikeuchi [20] and Huynh et al [14] exploit the polarization of refracted light to estimate transparent surfaces. A tomographic reconstruction of transparent solids from multiple images was proposed by Trifonov et al [32].…”

Section: Related Workmentioning

confidence: 99%

Refractive shape from light field distortion

Wetzstein

Roodnick

Heidrich

et al. 2011

2011 International Conference on Computer Vision

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Acquiring transparent, refractive objects is challenging as these kinds of objects can only be observed by analyzing the distortion of reference background patterns. We present a new, single image approach to reconstructing thin transparent surfaces, such as thin solids or surfaces of fluids. Our method is based on observing the distortion of light field background illumination. Light field probes have the potential to encode up to four dimensions in varying colors and intensities: spatial and angular variation on the probe surface; commonly employed reference patterns are only two-dimensional by coding either position or angle on the probe. We show that the additional information can be used to reconstruct refractive surface normals and a sparse set of control points from a single photograph.

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Inverse Polarization Raytracing: Estimating Surface Shapes of Transparent Objects

Cited by 56 publications

References 29 publications

Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography

Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography

Shape Estimation of Transparent Objects by Using Polarization Analyses

Refractive shape from light field distortion

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