The dimension of C1C^1 splines of arbitrary degree on a tetrahedral partition

We consider the linear space of piecewise polynomials in three variables which are globally smooth, i.e., trivariate $C^1$ splines. The splines are defined on a uniform tetrahedral partition $\Delta$, which is a natural generalization of the four-directional mesh. By using Bernstein-B{\´e}zier techniques, we establish formulae for the dimension of the $C^1$ splines of arbitrary degree

An emperical model for heterogeneous translucent objects

We introduce an empirical model for multiple scattering in heterogeneous translucent objects for which classical approximations such as the dipole approximation to the di usion equation are no longer valid. Motivated by the exponential fall-o of scattered intensity with distance, di use subsurface scattering is represented as a sum of exponentials per surface point plus a modulation texture. Modeling quality can be improved by using an anisotropic model where exponential parameters are determined per surface location and scattering direction. We validate the scattering model for a set of planar object samples which were recorded under controlled conditions and quantify the modeling error. Furthermore, several translucent objects with complex geometry are captured and compared to the real object under similar illumination conditions

A flexible and versatile studio for synchronized multi-view video recording

In recent years, the convergence of Computer Vision and Computer Graphics has put forth new research areas that work on scene reconstruction from and analysis of multi-view video footage. In free-viewpoint video, for example, new views of a scene are generated from an arbitrary viewpoint in real-time from a set of real multi-view input video streams. The analysis of real-world scenes from multi-view video to extract motion information or reflection models is another field of research that greatly benefits from high-quality input data. Building a recording setup for multi-view video involves a great effort on the hardware as well as the software side. The amount of image data to be processed is huge, a decent lighting and camera setup is essential for a naturalistic scene appearance and robust background subtraction, and the computing infrastructure has to enable real-time processing of the recorded material. This paper describes the recording setup for multi-view video acquisition that enables the synchronized recording of dynamic scenes from multiple camera positions under controlled conditions. The requirements to the room and their implementation in the separate components of the studio are described in detail. The efficiency and flexibility of the room is demonstrated on the basis of the results that we obtain with a real-time 3D scene reconstruction system, a system for non-intrusive optical motion capture and a model-based free-viewpoint video system for human actors.

A neighborhood-based approach for clustering of linked document collections

This technical report addresses the problem of automatically structuring linked document collections by using clustering. In contrast to traditional clustering, we study the clustering problem in the light of available link structure information for the data set (e.g., hyperlinks among web documents or co-authorship among bibliographic data entries). Our approach is based on iterative relaxation of cluster assignments, and can be built on top of any clustering algorithm (e.g., k-means or DBSCAN). These techniques result in higher cluster purity, better overall accuracy, and make self-organization more robust. Our comprehensive experiments on three different real-world corpora demonstrate the benefits of our approach

A custom designed density estimation method for light transport

We present a new Monte Carlo method for solving the global illumination problem in environments with general geometry descriptions and light emission and scattering properties. Current Monte Carlo global illumination algorithms are based on generic density estimation techniques that do not take into account any knowledge about the nature of the data points --- light and potential particle hit points --- from which a global illumination solution is to be reconstructed. We propose a novel estimator, especially designed for solving linear integral equations such as the rendering equation. The resulting single-pass global illumination algorithm promises to combine the flexibility and robustness of bi-directional path tracing with the efficiency of algorithms such as photon mapping

Combining linguistic and statistical analysis to extract relations from web documents

Search engines, question answering systems and classification systems alike can greatly profit from formalized world knowledge. Unfortunately, manually compiled collections of world knowledge (such as WordNet or the Suggested Upper Merged Ontology SUMO) often suffer from low coverage, high assembling costs and fast aging. In contrast, the World Wide Web provides an endless source of knowledge, assembled by millions of people, updated constantly and available for free. In this paper, we propose a novel method for learning arbitrary binary relations from natural language Web documents, without human interaction. Our system, LEILA, combines linguistic analysis and machine learning techniques to find robust patterns in the text and to generalize them. For initialization, we only require a set of examples of the target relation and a set of counterexamples (e.g. from WordNet). The architecture consists of 3 stages: Finding patterns in the corpus based on the given examples, assessing the patterns based on probabilistic confidence, and applying the generalized patterns to propose pairs for the target relation. We prove the benefits and practical viability of our approach by extensive experiments, showing that LEILA achieves consistent improvements over existing comparable techniques (e.g. Snowball, TextToOnto)

IO-Top-k: index-access optimized top-k query processing

Top-k query processing is an important building block for ranked retrieval, with applications ranging from text and data integration to distributed aggregation of network logs and sensor data. Top-k queries operate on index lists for a query's elementary conditions and aggregate scores for result candidates. One of the best implementation methods in this setting is the family of threshold algorithms, which aim to terminate the index scans as early as possible based on lower and upper bounds for the final scores of result candidates. This procedure performs sequential disk accesses for sorted index scans, but also has the option of performing random accesses to resolve score uncertainty. This entails scheduling for the two kinds of accesses: 1) the prioritization of different index lists in the sequential accesses, and 2) the decision on when to perform random accesses and for which candidates. The prior literature has studied some of these scheduling issues, but only for each of the two access types in isolation. The current paper takes an integrated view of the scheduling issues and develops novel strategies that outperform prior proposals by a large margin. Our main contributions are new, principled, scheduling methods based on a Knapsack-related optimization for sequential accesses and a cost model for random accesses. The methods can be further boosted by harnessing probabilistic estimators for scores, selectivities, and index list correlations. We also discuss efficient implementation techniques for the underlying data structures. In performance experiments with three different datasets (TREC Terabyte, HTTP server logs, and IMDB), our methods achieved significant performance gains compared to the best previously known methods: a factor of up to 3 in terms of execution costs, and a factor of 5 in terms of absolute run-times of our implementation. Our best techniques are close to a lower bound for the execution cost of the considered class of threshold algorithms

Overlap-aware global df estimation in distributed information retrieval systems

Peer-to-Peer (P2P) search engines and other forms of distributed information retrieval (IR) are gaining momentum. Unlike in centralized IR, it is difficult and expensive to compute statistical measures about the entire document collection as it is widely distributed across many computers in a highly dynamic network. On the other hand, such network-wide statistics, most notably, global document frequencies of the individual terms, would be highly beneficial for ranking global search results that are compiled from different peers. This paper develops an efficient and scalable method for estimating global document frequencies in a large-scale, highly dynamic P2P network with autonomous peers. The main difficulty that is addressed in this paper is that the local collections of different peers may arbitrarily overlap, as many peers may choose to gather popular documents that fall into their specific interest profile. Our method is based on hash sketches as an underlying technique for compact data synopses, and exploits specific properties of hash sketches for duplicate elimination in the counting process. We report on experiments with real Web data that demonstrate the accuracy of our estimation method and also the benefit for better search result ranking

Reflectance from images: a model-based approach for human faces

In this paper, we present an image-based framework that acquires the reflectance properties of a human face. A range scan of the face is not required. Based on a morphable face model, the system estimates the 3D shape, and establishes point-to-point correspondence across images taken from different viewpoints, and across different individuals' faces. This provides a common parameterization of all reconstructed surfaces that can be used to compare and transfer BRDF data between different faces. Shape estimation from images compensates deformations of the face during the measurement process, such as facial expressions. In the common parameterization, regions of homogeneous materials on the face surface can be defined a-priori. We apply analytical BRDF models to express the reflectance properties of each region, and we estimate their parameters in a least-squares fit from the image data. For each of the surface points, the diffuse component of the BRDF is locally refined, which provides high detail. We present results for multiple analytical BRDF models, rendered at novelorientations and lighting conditions