Archives for News & Reviews - Page 2

08 Jan

Predicting Visual Perception of Material Structure in Virtual Environments

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One of the most accurate yet still practical representation of material appearance is the Bidirectional Texture Function (BTF). The BTF can be viewed as an extension of Bidirectional Reflectance Distribution Function (BRDF) for additional spatial information that includes local visual effects such as shadowing, interreflection, subsurface-scattering, etc. However, the shift from BRDF to BTF represents not only a huge leap in respect to the realism of material reproduction, but also related high memory and computational costs stemming from the storage and processing of massive BTF data. In this work, we argue that each opaque material, regardless of its surface structure, can be safely substituted by a BRDF without the introduction of a significant perceptual error when viewed from an appropriate distance. Therefore, we ran a set of psychophysical studies over 25 materials to determine so-called critical viewing distances, i.e. the minimal distances at which the material spatial structure (texture) cannot be visually discerned. Our analysis determined such typical distances typical for several material categories often used in interior design applications. Furthermore, we propose a combination of computational features that can predict such distances without the need for a psychophysical study. We show that our work can significantly reduce rendering costs in applications that process complex virtual scenes.One of the most accurate yet still practical representation of material appearance is the Bidirectional Texture Function (BTF). The BTF can be viewed as an extension of Bidirectional Reflectance Distribution Function (BRDF) for additional spatial information that includes local visual effects such as shadowing, interreflection, subsurface-scattering, etc.
08 Jan

Accurate and Efficient Computation of Laplacian Spectral Distances and Kernels

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This paper introduces the Laplacian spectral distances, as a function that resembles the usual distance map, but exhibits properties (e.g. smoothness, locality, invariance to shape transformations) that make them useful to processing and analysing geometric data. Spectral distances are easily defined through a filtering of the Laplacian eigenpairs and reduce to the heat diffusion, wave, biharmonic and commute-time distances for specific filters. In particular, the smoothness of the spectral distances and the encoding of local and global shape properties depend on the convergence of the filtered eigenvalues to zero. Instead of applying a truncated spectral approximation or prolongation operators, we propose a computation of Laplacian distances and kernels through the solution of sparse linear systems. Our approach is free of user-defined parameters, overcomes the evaluation of the Laplacian spectrum and guarantees a higher approximation accuracy than previous work. This paper introduces the Laplacian spectral distances, as a function that resembles the usual distancemap, but exhibits properties (e.g. smoothness, locality, invariance to shape transformations) that make them useful to processing and analysing geometric data. Spectral distances are easily defined through a filtering of the Laplacian eigenpairs and reduce to the heat diffusion, wave, biharmonic and commute-time distances for specific filters. In particular, the smoothness of the spectral distances and the encoding of local and global shape properties depend on the convergence of the filtered eigenvalues to zero.
08 Jan

Towards Globally Optimal Normal Orientations for Large Point Clouds

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Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation-based approaches by reformulating the task as a graph-based energy minimization problem. By applying global solvers, we can achieve more consistent orientations than simple greedy optimizations. Furthermore, we present a streaming-based framework for orienting large point clouds. This framework orients patches locally and generates a globally consistent patch orientation on a reduced neighbour graph, which achieves similar quality to orienting the full graph. Various processing algorithms on point set surfaces rely on consistently oriented normals (e.g. Poisson surface reconstruction). While several approaches exist for the calculation of normal directions, in most cases, their orientation has to be determined in a subsequent step. This paper generalizes propagation-based approaches by reformulating the task as a graph-based energy minimization problem and presents a streaming-based out-of-core implementation.