An object model for computer graphics applications should contain two aspects of information: shape and reflectance properties of the object. A number of techniques have been developed for modeling object shapes by observing real objects. In contrast, attempts to model reflectance properties of real objects have been rather limited. In most cases, modeled reflectance properties are too simple or too complicated to be used for synthesizing realistic images of the object. In this paper, we propose a new method for modeling object reflectance properties, as well as object shapes, by observing real objects. First, an object surface shape is reconstructed by merging multiple range images of the object. By using the reconstructed object shape and a sequence of color images of the object, parameters of a reflection model are estimated in a robust manner. The key point of the proposed method is that, first, the diffuse and specular reflection components are separated from the color image sequence, and then, reflectance parameters of each reflection component are estimated separately. This approach enables estimation of reflectance properties of real objects whose surfaces show specularity as well as diffusely reflected lights. The recovered object shape and reflectance properties are then used for synthesizing object images with realistic shading effects under arbitrary illumination conditions.

In this paper, we present a new method to recover an approximation of the bidirectional reflectance distribution function (BRDF) of the surfaces present in a real scene. This is done from a single photograph and a 3D geometric model of the scene. The result is a full model of the reflectance properties of all surfaces, which can be rendered under novel illumination conditions with, for example, viewpoint modification and the addition of new synthetic objects. Our technique produces a reflectance model using a small number of parameters. These parameters nevertheless approximate the BRDF and allow the recovery of the photometric properties of diffuse, specular, isotropic or anisotropic textured objects. The input data are a geometric model of the scene including the light source positions and the camera properties, and a single image captured using this camera. Our algorithm generates a new synthetic image using classic rendering techniques, and a lambertian hypothesis about the reflectance model of the surfaces. Then, it iteratively compares the original image to the new one, and chooses a more complex reflectance model if the difference between the two images is greater than a user-defined threshold.

. In this paper we propose a new approach for mapping and blending textures on 3D geometries. The system starts from a 3D mesh which represents a real object and improves this model with pictorial detail. Texture detail is acquired via a common photographic process directly from the real object. These images are then registered and stitched on the 3D mesh, by integrating them into a single standard texture map. An optimal correspondence between regions of the 3D mesh and sections of the acquired images is built. Then, a new approach is proposed to produce a smooth join between different images that map on adjacent sections of the surface, based on texture blending. For each mesh face which is on the adjacency border between different observed images, a corresponding triangular texture patch is resampled as a weighted blend of the corresponding adjacent images sections. The accuracy of the resampling and blending process is improved by computing an accurate piecewise local r...

For synthesizing realistic images of a real three dimensional object, reflectance properties of the object surface, as well as the object shape, need to be measured. This paper describes one approach to create a three dimensional object model with physically correct reflectance properties by observing a real object. The approach consists of three steps. First, a sequence of range images and color images is measured by rotating a real object on a rotary table with fixed viewing and illumination directions. Then, the object shape is obtained as a collection of triangular patches by merging multiple range images. Secondly, by using the recovered object shape, color pixel intensities of the color image sequence are mapped to the object surface and separated into the diffuse and specular reflection components. Finally, the separated reflection components are used to estimate parameters of the Lambertian reflection model and a simplified Torrance-Sparrow reflection model. We have successfully tested our approach using images of a real object. Synthesized images of the object under arbitrary illumination conditions are shown in this paper.

A new system for the construction of highly realistic models of real free-form 3D objects is proposed, based on the integration of several techniques (automatic 3D scanning, inverse illumination, inverse texture-mapping and textured 3D graphics).Our system improves the quality of a 3D model (e.g. acquired with a range scanning device) by adding color detail and, if required, high frequency shape detail.Detail is obtained by processing a set of digital photographs of the object.This is carried out by performing several subtasks: to compute camera calibration and position, to remove illumination effects obtaining both illumination-invariant reflectance properties and a high resolution surface normal field, and finally to blend and stitch the acquired detail on the triangle mesh via standard texture mapping.In particular, the smooth join between different images that map on adjacent sections of the surface is obtained by applying an accurate piecewise local registration of the original images and by blending textures.For each mesh face which is on the adjacency border between different observed images, a corresponding triangular texture patch can also be resampled as a weighted blend of the corresponding adjacent image sections.Examples of the results obtained with sample works of art are presented and discussed.

This thesis addresses the problem of estimating the surface reflection model of objects observed in a terrestrial scene, illuminated by natural illumination; that is, a scene which is illuminated by sun and sky light alone. This is a departure from the traditional analysis of laboratory scenes, which are illuminated by idealised light sources with positions and radiance distributions that are precisely controlled. Natural illumination presents a complex hemispherical light source which changes in both spatial and spectral distribution with time, terrestrial location, and atmospheric conditions. An image-based approach to the measurement of surface reflection is presented. The use of a sequence of images, taken over a period of time, allows the varying reflection from the scene due to the changing natural illumination to be measured. It is shown that the temporal change in image pixel values is suitable for the parameters of a reflection model to be estimated. These parameters are estim...

Abstract We propose a generative model based method for recovering both the shape and the reflectance of the surface(s) of a scene from multiple images, assuming that illumination conditions and cameras calibration are known in advance. Based on a variational framework and via gradient descents, the algorithm minimizes simultaneously and consistently a global cost functional with respect to both shape and reflectance. The motivations for our approach are threefold. (1) Contrary to previous works which mainly consider specific individual scenarios, our method applies indiscriminately to a number of classical scenarios; in particular it works for classical stereovision, multiview photometric stereo and multiview shape from shading. It works with changing as well as static illumination. (2) Our approach naturally combines stereo, silhouette and shading cues in a single framework. (3) Moreover, unlike most previous methods dealing with only Lambertian surfaces, the proposed

To appropriately reproduce a real object in a mixed environment, it is necessary to estimate reflectance properties of object surfaces. This paper describes a new method of densely estimating non-uniform surface reflectance properties of an object with convex and concave surfaces using registered range and surface color texture images obtained by a laser rangefinder. The proposed method determines positions of light source to take color images for discriminating diffuse and specular reflection components of surface reflection. The Torrance-Sparrow model is employed to estimate reflectance parameters using color images under multiple illumination conditions. Experiments show the usefulness of the method.

Abstract. In this paper, a generative model based method for recovering both the shape and the reflectance of the surface(s) of a scene from multiple images is presented, assuming that illumination conditions are known in advance. Based on a variational framework and via gradient descents, the algorithm minimizes simultaneously and consistently a global cost functional with respect to both shape and reflectance. Contrary to previous works which consider specific individual scenarios, our method applies to a number of scenarios – mutiview stereovision, multiview photometric stereo, and multiview shape from shading. In addition, our approach naturally combines stereo, silhouette and shading cues in a single framework and, unlike most previous methods dealing with only Lambertian surfaces, the proposed method considers general dichromatic surfaces. 1 Introduction and Related Work Many methods have been proposed to recover the three-dimensional surface shape using multiple images during these last two decades [1]. On the other hand, for a long time, the estimation of surface radiance/reflectance was secondary. Even some recent

In this report, we present a new method to recover an approximation of the bidirectional reAEectance distribution function (BRDF) of the surfaces present in a real scene. This is done from a single photograph and a 3D geometric model of the scene. The result is a full model of the reAEectance properties of all surfaces, which can be rendered under novel illumination conditions, with, for example, viewpoint modication and the addition of new synthetic objects. Our technique produces a reAEectance model using a small number of parameters. Using a new hierarchical algorithm assumptions are done for each surface about their BRDF, which becomes more and more complex during the rerendering iterations. We treat each type of BRDF separately, applying a specic algorithm to take into account its own characteristics and its parameters. The number of parameters varies with the complexity of the model.