3D object shape and reflectance property reconstruction using image scanner

Ukida, Hiroyuki; Tanimoto, Yasuhiro; Sano, Tetsuya; Yamamoto, Hideki

doi:10.1109/ist.2009.5071610

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Our method is non-iterative, since the user marks these different regions with sketches. Estimation methods also vary according to the material model used, from higher dimensional BRDFs [29] to the Torrance-Sparrow [26] or Phong models [31]. We follow approaches based on the Phong parametric model.…”

Section: Related Workmentioning

confidence: 99%

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Sketch-based warping of RGBN images

et al. 2011

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While current image deformation methods are careful in making the new geometry seem right, little attention has been given to the photometric aspects. We introduce a deformation method that results in coherently illuminated objects. For this task, we use RGBN images to support a relighting step integrated in a sketch-based deformation method. We warp not only colors but also normals. Normal warping requires smooth warping fields. We use sketches to specify sparse warping samples and impose additional constraints for region of interest control. To satisfy these new constraints, we present a novel image warping method based on Hermite-Birkhoff interpolation with radial basis functions that results in a smooth warping field. We also use sketches to help the system identify both lighting conditions and material from single images. We present results with RGBN images from different sources, including photometric stereo, synthetic images, and photographs.

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Section: Related Workmentioning

confidence: 99%

“…Our approach is similar to that of Ukida et al [31], but we only have a single image. Hence, we assume the material is spatially constant obtaining enough equations.…”

Section: Light and Materials Estimationmentioning

confidence: 99%

Sketch-based warping of RGBN images

et al. 2011

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“…But, this method needs many scanned images. To overcome this problem, we developed another shape reconstruction method using only four images taken by linear light sources [4]. This method can estimate object shape approximately without the influence of the intensity of specular components on the object surface.…”

Section: Introductionmentioning

confidence: 99%

3D object reconstruction using multiple linear light sources in image scanner

Ukida

2011

2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)

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This paper discusses a 3D shape measurement device based on an image scanner. Our shape reconstruction method using the image scanner is one of the photometric stereo method applied to multiple linear light sources. This method is constructed by several processes. First, we estimate the heights of object from the scanning plane to object's surface using four images taken by two pairs of two linear light sources which are located in same direction. And then, the surface normal vector, color and specular components are estimated by using several images taken by light sources located in a circular path. In experiments, we investigate optimal number and locations of light sources to estimate object shape, color and specular components.

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“…But, this method needs many scanned images. To overcome this problem, we developed another shape reconstruction method using only four images taken by linear light sources [7]. This method can estimate object shape approximately without the influence of the intensity of specular components on the object surface.…”

Section: Introductionmentioning

confidence: 99%

3D shape scanner using multiple light sources

Ukida

Tanimoto²,

Sano³

et al. 2010

2010 IEEE Instrumentation &Amp; Measurement Technology Conference Proceedings

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This study proposes a method to recover 3D shape, color and specular reflections of an object from images taken by an image scanner which has multiple illuminations. The proposed method is based on the photometric stereo method and it is constructed by several processes. First is the initial shape estimation using four images, and then the color and specular component estimation are performed sequentially. Finally, we apply the non-linear least square method to estimate optimal parameters of shape, color and specular components. In experiments, we perform the object reconstruction using synthetic images. Estimated color and specular components of object has some errors but the object shape can be estimated accurately.

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3D object shape and reflectance property reconstruction using image scanner

Cited by 4 publications

References 8 publications

Sketch-based warping of RGBN images

Sketch-based warping of RGBN images

3D object reconstruction using multiple linear light sources in image scanner

3D shape scanner using multiple light sources

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