Measuring the Shape and Reflectance of Real Objects for Reproducing the Material Appearance (original) (raw)
Related papers
Image-Based Acquisition of Shape and Spatially Varying Reflectance
Procedings of the British Machine Vision Conference 2008, 2008
The shape and reflectance of complex objects, for use in computer graphics applications, cannot always be acquired using specialized equipment due to cost or pratical considerations. We want to provide an easy and cost-effective way for the approximate recovery of both shape and spatially-varying reflectance of objects using commodity hardware. In this paper, we present an image-based technique for recovering 3D shape and spatially-varying reflectance properties from a sparse set of photographs, taken under varying illumination. Our technique models the reflectance with a set of low-parameter BRDFs without knowledge of the location of the light-sources or camera. This results an a flexible and portable system that can be used in the field. We successfully apply the approach to several objects (synthetic and real), recovering shape and reflectance. The acquired information can then be used to render the object with modifications to geometry and lighting via traditional rendering methods.
Object Shape and Reflectance Modeling from Observation
Modeling from Reality, 2001
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.
Shape and Reflectance from RGB-D Images using Time Sequential Illumination
Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016
In this paper we propose a method for recovering the shape (geometry) and the diffuse reflectance from an image (or video) using a hybrid setup consisting of a depth sensor (Kinect), a consumer camera and a partially controlled illumination (using a flash). The objective is to show how combining RGB-D acquisition with a sequential illumination is useful for shape and reflectance recovery. A pair of two images are captured: one non flashed (image under ambient illumination) and a flashed one. A pure flash image is computed by subtracting the non flashed image from the flashed image. We propose an novel and near real-time algorithm, based on a local illumination model of our flash and the pure flash image, to enhance geometry (from the noisy depth map) and recover reflectance information.
Image-based reconstruction of spatial appearance and geometric detail
2003
Abstract Real-world objects are usually composed of a number of different materials that often show subtle changes even within a single material. Photorealistic rendering of such objects requires accurate measurements of the reflection properties of each material, as well as the spatially varying effects. We present an image-based measuring method that robustly detects the different materials of real objects and fits an average bidirectional reflectance distribution function (BRDF) to each of them.
Reflectance Analysis for 3D Computer Graphics Model Generation
Graphical Models and Image Processing, 1996
suming and can be a bottle neck for realistic image syn-For synthesizing realistic images of a real three dimensional thesis. Therefore, techniques to obtain object models object, reflectance properties of the object surface, as well as automatically by observing a real object could have a great the object shape, need to be measured. This paper describes impact in practical applications. one approach to create a three dimensional object model with Techniques for measuring the geometric information by physically correct reflectance properties by observing a real using range data from real objects have received much object. The approach consists of three steps. First, a sequence attention recently. Turk and Levoy developed a system of range images and color images is measured by rotating a which can merge multiple surface meshes one by one, using real object on a rotary table with fixed viewing and illumination two step strategy: registration by the iterative closest-point directions. Then, the object shape is obtained as a collection algorithm (ICP algorithm) and integration by the zippering of triangular patches by merging multiple range images. Second, by using the recovered object shape, color pixel intensities algorithm [23]. Higuchi et al. have developed a method of the color image sequence are mapped to the object surface for merging multiple range views of a free-form surface and separated into the diffuse and specular reflection compoobtained from arbitrary viewing directions, with no initial nents. Finally, the separated reflection components are used to estimation of relative transformation among those viewing estimate parameters of the Lambertian reflection model and a directions [6]. The method is based on the spherical attrisimplified Torrance-Sparrow reflection model. We have sucbute image (SAI) representation of free-form surfaces cessfully tested our approach using images of a real object. which was originally introduced by Delingette et al. in [4]. Synthesized images of the object under arbitrary illumination Hoppe et al. [7] have introduced an algorithm to construct conditions are shown in this paper.
Reflectance and texture of real-world surfaces
ACM Transactions on Graphics, 1999
In this work, we investigate the visual appearance of real-world surfaces and the dependence of appearance on the geometry of imaging conditions. We discuss a new texture representation called the BTF (bidirectional texture function) which captures the variation in texture with illumination and viewing direction. We present a BTF database with image textures from over 60 different samples, each observed with over 200 different combinations of viewing and illumination directions. We describe the methods involved in collecting the database as well as the importqance and uniqueness of this database for computer graphics. A related quantity to the BTF is the familiar BRDF (bidirectional reflectance distribution function). The measurement methods involved in the BTF database are conducive to simultaneous measurement of the BRDF. Accordingly, we also present a BRDF database with reflectance measurements for over 60 different samples, each observed with over 200 different combinations of v...
Integrated three-dimensional shape and reflection properties measurement system
Applied Optics, 2011
Creating accurate three-dimensional (3D) digitalized models of cultural heritage objects requires that information about surface geometry be integrated with measurements of other material properties like color and reflectance. Up until now, these measurements have been performed in laboratories using manually integrated (subjective) data analyses. We describe an out-of-laboratory bidirectional reflectance distribution function (BRDF) and 3D shape measurement system that implements shape and BRDF measurement in a single setup with BRDF uncertainty evaluation. The setup aligns spatial data with the angular reflectance distribution, yielding a better estimation of the surface's reflective properties by integrating these two modality measurements into one setup using a single detector. This approach provides a better picture of an object's intrinsic material features, which in turn produces a higher-quality digitalized model reconstruction. Furthermore, this system simplifies the data processing by combining structured light projection and photometric stereo. The results of our method of data analysis describe the diffusive and specular attributes corresponding to every measured geometric point and can be used to render intricate 3D models in an arbitrarily illuminated scene.
Lecture Notes in Computer Science, 2010
The paper presents a complete methodology for processing sets of data registered by the means of a measurement system providing integrated 3D shape, multispectral color and angular reflectance characteristic. The data comprise of clouds of points representing the shape of the measured object, a set of intensity responses as a function of wavelength of incident light used for color calculation and a set of distributions of reflected intensity as a function of illumination and observation angles. Presented approach allows to create a complete 3D model of the measured object which preserves the object's shape, color and reflectivity properties. It is developed specifically for application in the digitization of cultural heritage objects for storing and visualization purposes, as well as duplication by the means of 3D printing technology.
Linear light source reflectometry
ACM Transactions on Graphics, 2003
This paper presents a technique for estimating the spatially-varying reflectance properties of a surface based on its appearance during a single pass of a linear light source. By using a linear light rather than a point light source as the illuminant, we are able to reliably observe and estimate the diffuse color, specular color, and specular roughness of each point of the surface. The reflectometry apparatus we use is simple and inexpensive to build, requiring a single direction of motion for the light source and a fixed camera viewpoint. Our model fitting technique first renders a reflectance table of how diffuse and specular reflectance lobes would appear under moving linear light source illumination. Then, for each pixel we compare its series of intensity values to the tabulated reflectance lobes to determine which reflectance model parameters most closely produce the observed reflectance values. Using two passes of the linear light source at different angles, we can also estimate per-pixel surface normals as well as the reflectance parameters. Additionally our system records a per-pixel height map for the object and estimates its per-pixel translucency. We produce real-time renderings of the captured objects using a custom hardware shading algorithm. We apply the technique to a test object exhibiting a variety of materials as well as to an illuminated manuscript with gold lettering. To demonstrate the technique's accuracy, we compare renderings of the captured models to real photographs of the original objects.
Example-based reflectance estimation for capturing relightable models of people
IET 5th European Conference on Visual Media Production (CVMP 2008), 2008
We present a new approach to reflectance estimation for dynamic scenes. Non-parametric image statistics are used to transfer reflectance properties from a static example set to a dynamic image sequence. The approach allows reflectance estimation for surface materials with inhomogeneous appearance, such as those which commonly occur with patterned or textured clothing. Material reflectance properties are initially estimated from static images of the subject under multiple directional illuminations using photometric stereo. The estimated reflectance together with the corresponding image under uniform ambient illumination form a prior set of reference material observations. Material reflectance properties are then estimated for video sequences of a moving person captured under uniform ambient illumination by matching the observed local image statistics to the reference observations. Results demonstrate that the transfer of reflectance properties enables estimation of the dynamic surface normals and subsequent relighting. This approach overcomes limitations of previous work on material transfer and relighting of dynamic scenes which was limited to surfaces with regions of homogeneous reflectance. We evaluate for relighting 3D model sequences reconstructed from multiple view video. Comparison to previous model relighting demonstrates improved reproduction of detailed texture and shape dynamics.