Painting canvas synthesis

We present the development of a procedural method for texturing and modeling different kinds of woven canvas used to support easel paintings. The detailed macro- and microgeometry of textiles and different weaving patterns found in woven fabrics is conveniently simulated by procedural displacement and surface shading. The common varieties of canvas used in art production since the Italian Renaissance period are presented and recreated. The anatomy of an oil-based painting is briefly introduced and a visual simulation of decay presented. We also apply our texturing and shading techniques to a simple geometric representation of painting to help visualize the changing characteristics developed during the aging process of a canvas support kept in uncontrolled environmental conditions.     

Geometry‐Aware Framebuffer Level of Detail

This paper introduces a framebuffer level of detail algorithm for controlling the pixel workload in an interactive rendering application. Our basic strategy is to evaluate the shading in a low resolution buffer and, in a second rendering pass, resample this buffer at the desired screen resolution. The size of the lower resolution buffer provides a trade‐off between rendering time and the level of detail in the final shading. In order to reduce approximation error we use a feature‐preserving reconstruction technique that more faithfully approximates the shading near depth and normal discontinuities. We also demonstrate how intermediate components of the shading can be selectively resized to provide finer‐grained control over resource allocation. Finally, we introduce a simple control mechanism that continuously adjusts the amount of resizing necessary to maintain a target framerate. These techniques do not require any preprocessing, are straightforward to implement on modern GPUs, and are shown to provide significant performance gains for several pixel‐bound scenes.     

Efficient sparse voxel octrees

In this paper, we examine the possibilities of using voxel representations as a generic way for expressing complex and feature-rich geometry on current and future GPUs. We present in detail a compact data structure for storing voxels and an efficient algorithm for performing ray casts using this structure. We augment the voxel data with novel contour information that increases geometric resolution, allows more compact encoding of smooth surfaces, and accelerates ray casts. We also employ a novel normal compression format for storing high-precision object-space normals. Finally, we present a variable-radius postprocess filtering technique for smoothing out blockiness caused by discrete sampling of shading attributes. Based on benchmark results, we show that our voxel representation is competitive with triangle-based representations in terms of ray casting performance, while allowing tremendously greater geometric detail and unique shading information for every voxel. Our voxel codebase is open sourced and available at http://code.google.com/p/efficient-sparse-voxel-octrees/.     

Modeling 3D Euclidean geometry

This article compares five models of 3D Euclidean geometry-not theoretically, but by demonstrating how to implement a simple recursive ray tracer in each of them. It's meant as a tangible case study of the profitability of choosing an appropriate model, discussing the trade-offs between elegance and performance for this particular application. The models we compare are 3D linear algebra, 3D geometric algebra, 4D linear algebra, 4D geometric algebra, and 5D geometric algebra.     

Multiple illuminant direction detection with application to imagesynthesis

Pentland observed (1982, 1984) that the human eye is sensitive to the change of intensities. On an image of a smooth surface, the change of intensities is maximal whenever the illuminant direction is perpendicular to the normal of the surface. This motivates us to introduce the concept of critical points, where the surface normal is perpendicular to some light source direction. Apparently, the illuminant direction has a simple geometric relationship with the corresponding critical points. In this paper, for simplicity reasons, we restrict our discussions to the shading of a Lambertian sphere of known size in a multiple distant light source environment. A novel global representation of the intensity function is derived. Based on this intensity characterization, the least-squares and iteration techniques are used to determine critical points and, thus, the light source directions and their intensities if certain conditions are satisfied. The performance of this new approach is evaluated using both synthetic images and real images. As an application, we use it as a tool to determine light sources in real image synthesis. The experimental results show that this technique can be used to superimpose synthetic objects with a real scene     

Image Warping for Shape Recovery and Recognition

We demonstrate that, for a large class of reflectance functions, there is a direct relationship between image warps and the corresponding geometric deformations of the underlying three-dimensional objects. This helps explain the hidden geometrical assumptions in object recognition schemes which involve two-dimensional image warping computed by matching image intensity. In addition, it allows us to propose a novel variant of shape from shading which we call shape from image warping. The idea is that the three-dimensional shape of an object is estimated by determining how much the image of the object is warped with respect to the image of a known prototype shape. Therefore, detecting the image warp relative to a prototype of known shape allows us to reconstruct the shape of the imaged object. We derive properties of these shape warps and illustrate the results by recovering the shapes of faces.     

Parameterization,Feature Extraction and Binary Encoding for the Visualization of Tree-Like Structures

The study of vascular structures, using medical 3D models, is an active field of research. Illustrative visualizations have been applied to this domain in multiple ways. Researchers made the geometric properties of vasculature more comprehensive and augmented the surface with representations of multivariate clinical data. Techniques that head beyond the application of colour-maps or simple shading approaches require a surface parameterization, that is, texture coordinates, in order to overcome locality. When extracting 3D models, the computation of texture coordinates on the mesh is not always part of the data processing pipeline. We combine existing techniques to a simple parameterization approach that is suitable for tree-like structures. The parameterization is done w.r.t. to a pre-defined source vertex. For this, we present an automatic algorithm, that detects the tree root. The parameterization is partly done in screen-space and recomputed per frame. However, the screen-space computation comes with positive features that are not present in object-space approaches. We show how the resulting texture coordinates can be used for varying hatching, contour parameterization, display of decals, as additional depth cues and feature extraction. A further post-processing step based on parameterization allows for a segmentation of the structure and visualization of its tree topology.     

The design and implementation of a Bayesian CAD modeler for robotic applications

We present a Bayesian CAD modeler for robotic applications. We address the problem of taking into account the propagation of geometric uncertainties when solving inverse geometric problems. The proposed method may be seen as a generalization of constraint-based approaches in which we explicitly model geometric uncertainties. Using our methodology, a geometric constraint is expressed as a probability distribution on the system parameters and the sensor measurements, instead of a simple equality or inequality. Tosolve geometric problems in this framework, we propose an original resolution method able to adapt to problem complexity. Using two examples, we show how to apply our approach by providing simulation results using our modeler.     

Recovering intrinsic images from a single image

Interpreting real-world images requires the ability distinguish the different characteristics of the scene that lead to its final appearance. Two of the most important of these characteristics are the shading and reflectance of each point in the scene. We present an algorithm that uses multiple cues to recover shading and reflectance intrinsic images from a single image. Using both color information and a classifier trained to recognize gray-scale patterns, given the lighting direction, each image derivative is classified as being caused by shading or a change in the surface's reflectance. The classifiers gather local evidence about the surface's form and color, which is then propagated using the Generalized Belief Propagation algorithm. The propagation step disambiguates areas of the image where the correct classification is not clear from local evidence. We use real-world images to demonstrate results and show how each component of the system affects the results.     

Adaptive texture space shading for stochastic rendering

When rendering effects such as motion blur and defocus blur, shading can become very expensive if done in a naïve way, i.e. shading each visibility sample. To improve performance, previous work often decouple shading from visibility sampling using shader caching algorithms. We present a novel technique for reusing shading in a stochastic rasterizer. Shading is computed hierarchically and sparsely in an object‐space texture, and by selecting an appropriate mipmap level for each triangle, we ensure that the shading rate is sufficiently high so that no noticeable blurring is introduced in the rendered image. Furthermore, with a two‐pass algorithm, we separate shading from reuse and thus avoid GPU thread synchronization. Our method runs at real‐time frame rates and is up to 3 × faster than previous methods. This is an important step forward for stochastic rasterization in real time.     

Solving aliasing from shading with selective shader supersampling

Existing GPU antialiasing techniques, such as MSAA or MLAA, focus on reducing aliasing artifacts along silhouette boundaries or edges in image space. However, they neglect aliasing from shading in case of high-frequency geometric detail. This may lead to a shading aliasing artifact that resembles Bailey's Bead Phenomenon—the degradation of continuous specular highlights to a string of pearls. These types of artifacts are particularly striking for high-quality surfaces. So far, the only way of removing aliasing from shading is by globally supersampling the entire image with a large number of samples. However, globally supersampling the image is slow and significantly increases bandwidth consumption. We propose three adaptive approaches that locally supersample triangles only where necessary on the GPU. Thereby, we efficiently remove artifacts from shading while aliasing along silhouettes is reduced by efficient hardware MSAA.     

Restoring warped document images through 3D shape modeling

Scanning a document page from a thick bound volume often results in two kinds of distortions in the scanned image, i.e., shade along the "spine" of the book and warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. From a technical point of view, this shape from shading (SFS) problem in real-world environments is characterized by 1) a proximal and moving light source, 2) Lambertian reflection, 3) nonuniform albedo distribution, and 4) document skew. Taking all these factors into account, we first build practical models (consisting of a 3D geometric model and a 3D optical model) for the practical scanning conditions to reconstruct the 3D shape of the book surface. We next restore the scanned document image using this shape based on deshading and dewarping models. Finally, we evaluate the restoration results by comparing our estimated surface shape with the real shape as well as the OCR performance on original and restored document images. The results show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.     

Revised definition of optical flow: integration of radiometric andgeometric cues for dynamic scene analysis

Optical flow has been commonly defined as the apparent motion of image brightness patterns in an image sequence. In this paper, we propose a revised definition to overcome shortcomings in interpreting optical flow merely as a geometric transformation field. The new definition is a complete representation of geometric and radiometric variations in dynamic imagery. We argue that this is more consistent with the common interpretation of optical flow induced by various scene events. This leads to a general framework for the investigation of problems in dynamic scene analysis, based on the integration and unified treatment of both geometric and radiometric cues in time-varying imagery. We discuss selected models, including the generalized dynamic image model, for the estimation of optical flow. We show how various 3D scene information are encoded in, and thus may be extracted from, the geometric and radiometric components of optical flow. We provide selected examples based on experiments with real images     

Impossible and ambiguous shading patterns

A smooth object depicted in a monochrome image will often exhibit brightness variation, or shading. A problem much studied in computer vision has been that of how object shape may be recovered from image shading. When the imaging conditions are such that an overhead point-source illuminates a smooth Lambertian surface, the problem may be formulated as that of finding a solution to an eikonal equation. This article will focus on the existence and uniqueness of such solutions, reporting recent results obtained. With regard to existence, shading patterns are exhibited for which there is no corresponding object shape. Specifically, a necessary and sufficient condition is presented for a circularly symmetric eikonal equation to admit exclusively unbounded solutions; additionally, a sufficient condition is given for an eikonal equation to have no solution whatsoever. In connection with uniqueness, we consider eikonal equations, defined over a disc, such that the Euclidean norm of the gradient of any solution is circularly symmetric, vanishes exactly at the disc center, and diverges to infinity as the circumference of the disc is approached. Contrary to earlier influential work, a class of such eikonal equations is shown to possess simultaneously circularly symmetric and noncircularly symmetric bounded smooth solutions.     

一种投影仪相机系统几何配准的鲁棒算法

目前投影屏的反射及几何特性决定了投影仪投影图像的显示效果。为解除投影仪对标准屏幕的依赖性,借助数码相机可构成反馈系统对显示表面的缺陷进行辐射度补偿和图像校正,而其前提即是建立投影图像面与相机图像面像素之间的配准关系。为实现这一目的提出了一种能实现投影仪相机系统几何配准方法的鲁棒算法。通过设计黑白二色编码图像、显示采集编码图像的方式并对捕获图像进行二值化以及形态学处理以定位和识别采样色块,建立几何配准函数关系。该算法在平面、曲面以及无图案与有图案等各种显示表面的投影场景中得以验证,其平均配准误差稳定在0.2～1.0个像素单位,且不依赖于色块的数量而变化,并能在10s内完成整个配准关系的建立。实验表明该方法能在适用范围以及精度和效率等方面满足相机反馈式投影仪像面配准的应用需求。     

A model for volume lighting and modeling

Direct volume rendering is a commonly used technique in visualization applications. Many of these applications require sophisticated shading models to capture subtle lighting effects and characteristics of volumetric data and materials. For many volumes, homogeneous regions pose problems for typical gradient-based surface shading. Many common objects and natural phenomena exhibit visual quality that cannot be captured using simple lighting models or cannot be solved at interactive rates using more sophisticated methods. We present a simple yet effective interactive shading model which captures volumetric light attenuation effects that incorporates volumetric shadows, an approximation to phase functions, an approximation to forward scattering, and chromatic attenuation that provides the subtle appearance of translucency. We also present a technique for volume displacement or perturbation that allows realistic interactive modeling of high frequency detail for both real and synthetic volumetric data.     

Performance Modeling and Evaluation of MPI

Users of parallel machines need to have a good grasp for how different communication patterns and styles affect the performance of message-passing applications. LogGP is a simple performance model that reflects the most important parameters required to estimate the communication performance of parallel computers. The message passing interface (MPI) standard provides new opportunities for developing high performance parallel and distributed applications. In this paper, we use LogGP as a conceptual framework for evaluating the performance of MPI communications on three platforms: Cray-Research T3D, Convex Exemplar 1600SP, and a network of workstations (NOW). We develop a simple set of communication benchmarks to extract the LogGP parameters. Our objective in this is to compare the performance of MPI communication on several platforms and to identify a performance model suitable for MPI performance characterization. In particular, two problems are addressed: how LogGP quantifies MPI performance and what extra features are required for modeling MPI, and how MPI performance compare on the three computing platforms: Cray Research T3D, Convex Exemplar 1600SP, and workstations clusters.     

Shape recovery from shading by a new neural-based reflectance model.

We present a neural-based reflectance model of which the physical parameters of the reflectivity under different lighting conditions are interpreted by the network weights. The idea of our method is to optimize a proper reflectance model by an effective learning algorithm and to recover the object surface by a simple shape from shading recursive algorithm with this resulting model. Experiments, including synthetic and real images, were performed to demonstrate the performance of the proposed method for practical applications.