Rockhaven Waltz

https://digitalcommons.library.umaine.edu/mmb-me/1410/thumbnail.jp

Procedural Noise using Sparse Gabor Convolution

International audienceNoise is an essential tool for texturing and modeling. Designing interesting textures with noise calls for accurate spectral control, since noise is best described in terms of spectral content. Texturing requires that noise can be easily mapped to a surface, while high-quality rendering requires anisotropic filtering. A noise function that is procedural and fast to evaluate offers several additional advantages. Unfortunately, no existing noise combines all of these properties. In this paper we introduce a noise based on sparse convolution and the Gabor kernel that enables all of these properties. Our noise offers accurate spectral control with intuitive parameters such as orientation, principal frequency and bandwidth. Our noise supports two-dimensional and solid noise, but we also introduce setup-free surface noise. This is a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization. Our approach requires only a few bytes of storage, does not use discretely sampled data, and is nonperiodic. It supports anisotropy and anisotropic filtering. We demonstrate our noise using an interactive tool for noise design

A Fast Texture Synthesis Technique using Spatial Neighborhood

In this technical report, we present a texture synthesis technique that stores all possible histories in memory structure acting as a Finite State Machine. The histories, which are the most probable spatial neighborhood for each pixel or patch, are stored in each state of the FSM. After constructing that structure, the synthesis goes very fast and smooth in a raster scan order. We found that our technique works well for a wide variety of textures, ranging from stochastic to regular structured textures, including near-regular textures. We show that our technique is faster, and gives better results than previous pixel- or patch-based techniques, with minimal user input. Keywords: keyword1, Texture synthesis, Image processing, Pixel-based textures, Markov random field, and Image-based rendering

Feature-Based Texture Synthesis using Voronoi Diagrams Technical

In this technical report, we present a technique to synthesize featured textures easily and interactively. The main idea is to synthesize the texture by copying irregular patches from the source to the target texture, each of them containing a complete feature. The interior part of the feature is not touched while the cutting and stitching is done on the background texture between the features. The technique starts by selecting a feature in the source texture by the user, after which the algorithm finds the positions of other features, generates a similar distribution of features, and finally synthesises the target texture by copying and stitching patches of the target’s Voronoi cellular shapes from the source texture

The

The tile packing problem is a challenging combinatorial puzzle based on tiles with colored edges or colored corners. In its different incarnations, the puzzle gives rise to a number of interesting problems. In this paper, we sketch the background of the tile packing problem and present solutions to the puzzle. We hope that this work will stimulate further interest in this puzzle amongst readers, and that the remaining open problems will eventually be solved

Operations on Surfel-Bounded Solids

This report presents a simple local smoothing operator for this purpose. We illustrate that this operator enables us to obtain a more natural integration of the two solids added togethe