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Reflectance and texture of realworld surfaces
 ACM TRANS. GRAPHICS
, 1999
"... In this work, we investigate the visual appearance of realworld surfaces and the dependence of appearance on scale, viewing direction and illumination direction. At ne scale, surface variations cause local intensity variation or image texture. The appearance of this texture depends on both illumina ..."
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Cited by 586 (23 self)
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In this work, we investigate the visual appearance of realworld surfaces and the dependence of appearance on scale, viewing direction and illumination direction. At ne scale, surface variations cause local intensity variation or image texture. The appearance of this texture depends on both illumination and viewing direction and can be characterized by the BTF (bidirectional texture function). At su ciently coarse scale, local image texture is not resolvable and local image intensity is uniform. The dependence of this image intensity on illumination and viewing direction is described by the BRDF (bidirectional re ectance distribution function). We simultaneously measure the BTF and BRDF of over 60 di erent rough surfaces, each observed with over 200 di erent combinations of viewing and illumination direction. The resulting BTF database is comprised of over 12,000 image textures. To enable convenient use of the BRDF measurements, we t the measurements to two recent models and obtain a BRDF parameter database. These parameters can be used directly in image analysis and synthesis of a wide variety of surfaces. The BTF, BRDF, and BRDF parameter databases have important implications for computer vision and computer graphics and and each is made publicly available.
Depth estimation from image structure
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2002
"... AbstractÐIn the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges, and junctions may provide a 3D model of the scene but it will not provide infor ..."
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Cited by 111 (17 self)
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AbstractÐIn the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges, and junctions may provide a 3D model of the scene but it will not provide information about the actual ªscaleº of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, object recognition, under unconstrained conditions, remains difficult and unreliable for current computational approaches. Here, we propose a source of information for absolute depth estimation based on the whole scene structure that does not rely on specific objects. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene and, therefore, its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection. Index TermsÐDepth, image statistics, scene structure, scene recognition, scale selection, monocular vision. 1
Computing Local Surface Orientation and Shape from Texture for Curved Surfaces
, 1997
"... Shape from texture is best analyzed in two stages, analogous to stereopsis and structure from motion: (a) Computing the `texture distortion' from the image, and (b) Interpreting the `texture distortion' to infer the orientation and shape of the surface in the scene. We model the texture di ..."
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Cited by 106 (4 self)
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Shape from texture is best analyzed in two stages, analogous to stereopsis and structure from motion: (a) Computing the `texture distortion' from the image, and (b) Interpreting the `texture distortion' to infer the orientation and shape of the surface in the scene. We model the texture distortion for a given point and direction on the image plane as an affine transformation and derive the relationship between the parameters of this transformation and the shape parameters. We have developed a technique for estimating affine transforms between nearby image patches which is based on solving a system of linear constraints derived from a differential analysis. One need not explicitly identify texels or make restrictive assumptions about the nature of the texture such as isotropy. We use nonlinear minimization of a least squares error criterion to recover the surface orientation (slant and tilt) and shape (principal curvatures and directions) based on the estimated affine transforms in a number of different directions. A simple linear algorithm based on singular value decomposition of the linear parts of the affine transforms provides the initial guess for the minimization procedure. Experimental results on both planar and curved surfaces under perspective projection demonstrate good estimates for both orientation and shape. A sensitivity analysis yields predictions for both computer vision algorithms and human perception of shape from texture.
Single View Modeling of FreeForm Scenes
 IN PROC. OF CVPR
, 2002
"... This paper presents a novel approach for reconstructing freeform, texturemapped, 3D scene models from a single painting or photograph. Given a sparse set of userspecified constraints on the local shape of the scene, a smooth 3D surface that satisfies the constraints is generated. This problem is ..."
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Cited by 60 (0 self)
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This paper presents a novel approach for reconstructing freeform, texturemapped, 3D scene models from a single painting or photograph. Given a sparse set of userspecified constraints on the local shape of the scene, a smooth 3D surface that satisfies the constraints is generated. This problem is formulated as a constrained variational optimization problem. In contrast to previous work in single view reconstruction, our technique enables high quality reconstructions of freeform curved surfaces with arbitrary reflectance properties. A key feature of the approach is a novel hierarchical transformation technique for accelerating convergence on a nonuniform, piecewise continuous grid. The technique is interactive and updates the model in real time as constraints are added, allowing fast reconstruction of photorealistic scene models. The approach is shown to yield high quality results on a large variety of images.
Shapelets correlated with surface normals produce surfaces
 In ICCV05
, 2005
"... This paper addresses the problem of deducing the surface shape of an object given just the surface normals. Many shape measurement algorithms such as shape from shading and shape from texture only return the surface normals of an object, often with an ambiguity of π in the surface tilt. The surface ..."
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Cited by 44 (0 self)
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This paper addresses the problem of deducing the surface shape of an object given just the surface normals. Many shape measurement algorithms such as shape from shading and shape from texture only return the surface normals of an object, often with an ambiguity of π in the surface tilt. The surface shape has to be inferred from these normals, typically via some integration process. However, reconstruction through the integration of surface gradients is sensitive to noise and the choice of integration paths across the surface. In addition, existing techniques cannot accommodate ambiguities in tilt. This paper presents a new approach to the reconstruction of surfaces from surface normals using basis functions, referred to here as shapelets. The surface gradients of the shapelets are correlated with the gradients of the surface and the correlations summed to form the reconstruction. This results in a simple reconstruction process that is very robust to noise. Where there is an ambiguity of π in the surface tilt, reconstructions of reduced quality are still possible up to a positive/negative shape ambiguity. Intriguingly, some form of reconstruction is also possible using just slant information. 1.
PLANAR SURFACE ORIENTATION FROM TEXTURE SPATIAL FREQUENCIES
, 1995
"... This paper presents a computational model and a practical algorithm for determining the threedimensional orientation of a planar surface from visual texture information. The model consists of three parts: (1) a local spatial frequency based texture representation; (2) a model describing the projec ..."
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Cited by 36 (1 self)
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This paper presents a computational model and a practical algorithm for determining the threedimensional orientation of a planar surface from visual texture information. The model consists of three parts: (1) a local spatial frequency based texture representation; (2) a model describing the projection of surface texture to image texture; and (3) a solution of the texture projection model for the surface orientation under an assumption of surface texture homogeneity. The algorithm first measures the dominant frequency at each image point using three waveletlike transforms, and then finds the surface orientation that minimizes the variance of the image frequencies' backprojections. The algorithm is tested on photographs of realworld surfaces, exhibiting an average accuracy of better than 3 ° in slant and 4 ° in tilt. The current model and algorithm are more accurate, yet substantially simpler, than earlier versions of this approach.
The texture gradient equation for recovering shape from texture
 IEEE Trans. on Pattern Analysis and Machine Intelligence
"... AbstractÐThis paper studies the recovery of shape from texture under perspective projection. We regard Shape from Texture as a statistical estimation problem, the texture being the realization of a stochastic process. We introduce warplets, which generalize wavelets over the 2D affine group. At fine ..."
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Cited by 36 (1 self)
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AbstractÐThis paper studies the recovery of shape from texture under perspective projection. We regard Shape from Texture as a statistical estimation problem, the texture being the realization of a stochastic process. We introduce warplets, which generalize wavelets over the 2D affine group. At fine scales, the warpogram of the image obeys a transport equation, called Texture Gradient Equation. In order to recover the 3D shape of the surface, one must estimate the deformation gradient, which measures metric changes in the image. This is made possible by imposing a notion of homogeneity for the original texture, according to which the deformation gradient is equal to the velocity of the Texture Gradient Equation. By measuring the warplet transform of the image at different scales, we obtain a deformation gradient estimator. Index TermsÐShape from texture, texture gradient, warplets, wavelets. æ 1
Contour into Texture: Information Content of Surface Contours and Texture Flow
 Journal of the Optical Society of America, A
, 2001
"... Both surface contours and texture patterns can provide strong cues to the threedimensional shape of a surface in space. Many of the most perceptually salient texture patterns have a strong flowlike structure, resulting from the directional nature of the surface textures from which they project. Und ..."
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Cited by 26 (2 self)
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Both surface contours and texture patterns can provide strong cues to the threedimensional shape of a surface in space. Many of the most perceptually salient texture patterns have a strong flowlike structure, resulting from the directional nature of the surface textures from which they project. Under the minimal assumption that an oriented surface texture is homogeneous, the texture flow on a developable surface can be shown to follow parallel geodesics of the surface. The geometry of texture flow is therefore equivalent to that of an important class of surface contours: those that project from parallel geodesics of a developable surface. I derive a set of differential equations that support the estimation of surface shape from geodesic surface contours under spherical perspective, for both parallel and nonparallel contours. For perfectly oriented textures, the equations apply directly to the integrated flow lines in a texture image. For weakly oriented textures, perspective projection distorts the projected orientation of flow lines away from the idealized case of pure contours; however, simulations show that for a large class of textures, these distortions will be small and limited largely to extreme surface poses. The geometrical analysis, along with a number of phenomenal demonstrations and psychophysical results, suggests that the human visual system coopts shape from contour mechanisms to estimate surface shape from texture flow. © 2001 Optical Society of America OCIS codes: 330.4060, 330.5020, 150.0150. 1.
Adding depth to cartoons using sparse depth (in)equalities
 IN EUROGRAPHICS
, 2010
"... This paper presents a novel interactive approach for adding depth information into handdrawn cartoon images and animations. In comparison to previous depth assignment techniques our solution requires minimal user effort and enables creation of consistent popups in a matter of seconds. Inspired by ..."
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Cited by 19 (0 self)
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This paper presents a novel interactive approach for adding depth information into handdrawn cartoon images and animations. In comparison to previous depth assignment techniques our solution requires minimal user effort and enables creation of consistent popups in a matter of seconds. Inspired by perceptual studies we formulate a custom tailored optimization framework that tries to mimic the way that a human reconstructs depth information from a single image. Its key advantage is that it completely avoids inputs requiring knowledge of absolute depth and instead uses a set of sparse depth (in)equalities that are much easier to specify. Since these constraints lead to a solution based on quadratic programming that is time consuming to evaluate we propose a simple approximative algorithm yielding similar results with much lower computational overhead. We demonstrate its usefulness in the context of a cartoon animation production pipeline including applications such as enhancement, registration, composition, 3D modelling and stereoscopic display.
Image Registration using Multiresolution Frequency Domain Correlation
 In Proceedings of the British Machine Vision Conference
, 1998
"... This paper describes a correlation based image registration method which is able to register images related by a single global affine transformation or by a transformation field which is approximately piecewise affine. The method has two key elements: an affine estimator, which derives estimates ..."
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Cited by 18 (3 self)
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This paper describes a correlation based image registration method which is able to register images related by a single global affine transformation or by a transformation field which is approximately piecewise affine. The method has two key elements: an affine estimator, which derives estimates of the six affine parameters relating two image regions by aligning their Fourier spectra prior to correlating; and a multiresolution search process, which determines the global transformation field in terms of a set of local affine estimates at appropriate spatial resolutions. The method is computationally efficient and performs well for a range of different images and transformations. 1 Introduction Image registration is an important area of Computer Vision and Image Processing. It involves determining the transformation which will map pixels in one image to their corresponding or matching pixels in one or more related images, where the latter are different views and/or different ti...