Texture growing on triangle mesh

Texture Mapping plays a very important role in Computer Graphics. Texture Synthesis is one of the main methods to obtain textures, it makes use of sample textures to generate new textures. Texture Transfer is based on Texture Synthesis, it renders objects with textures taken from different objects. Currently, most of Texture Synthesis and Transfer methods use a single sample texture. A method for Texture Synthesis adn Transfer from multi samples was presented. For texture synthesis, the L-shaped neighborhood seaching approach was used. Users specify the proportion of each sample, the number of seed points, and these seed points are scattered randomly according to their samples in horizontal and vertical direction synchronously to synthesize textures. The synthesized textures are very good. For texture transfer, the luminance of the target image and the sample textures are analyzed. This procedure is from coarse to fine, and can produce a visually pleasing result.

We present an efficient multiresolution block sampling-based texture synthesis algorithm that works well for a wide variety of challenging textures. We consider the texture synthesis as a procedure that first estimates and then resamples a probability function. To achieve the effect of good and fast estimation of the probability function for a given texture as well as to retain the structural information during synthesis, we propose a concept of block sampling and a corresponding novel scheme of texture synthesis based on multiresolution block sampling (MBS). The computational complexity of the proposed algorithm is much lower than that of existing algorithms. Experimental results have shown that his novel texture synthesis algorithm produces superior results for many challenging textures.

Many natural and man-made surface patterns are created by interactions between texture elements and surface geometry. We believe that the best way to create such patterns is to synthesize a texture directly on the surface of the model. Given a texture sample in the form of an image, we create a similar texture over an irregular mesh hierarchy that has been placed on a given surface.Our method draws upon texture synthesis methods that use image pyramids, and we use a mesh hierarchy to serve in place of such pyramids. First, we create a hierarchy of points from low to high density over a given surface, and we connect these points to form a hierarchy of meshes. Next, the user specifies a vector field over the surface that indicates the orientation of the texture. The mesh vertices on the surface are then sorted in such a way that visiting the points in order will follow the vector field and will sweep across the surface from one end to the other. Each point is then visited in turn to determine its color. The color of a particular point is found by examining the color of neighboring points and finding the best match to a similar pixel neighborhood in the given texture sample. The color assignment is done in a coarse-to-fine manner using the mesh hierarchy. A texture created this way fits the surface naturally and seamlessly.

An improved image texture synthesis based on algorithm convolution neural network

In the research of virtual hepatic surgery, the synthesis of hepatic texture space and the texture mapping of the hepatic body model are inseparable. Different methods for hepatic volume texture on the authenticity and mapping speed tend to have much difference. Based on computer unified device architecture (CUDA), this paper tries to present a texture synthesis and mapping method for the accelerating of hepatic body, which is used to solve the problem that traditional method under the hepatic volumetric texture synthesis and mapping process cost too much time. This new method may eliminate the traditional serial correlation of texture synthesis. What’s more, it may improve the efficiency of texture synthesis when the multithreading parallel processing for synthesis in the hepatic volumetric texture space in selecting and distributing blocks based on CUDA architecture.

Progressive texture synthesis can provide an added functional advantage to existing texture synthesis algorithms, which are time consuming and fail to deliver in some application areas. To provide practical solutions to this challenge we have previously proposed a discrete wavelet transform (DWT) based texture synthesis algorithm for 2D surfaces. In this paper we propose the extension of this approach to 3D progressive-texture synthesis. The proposed 3D progressive- texture synthesis algorithm makes use of the EZW (embedded zero-tree wavelet) idea proposed by Shapiro, which is capable of prioritising the coefficients of a DWT decomposed image according to their visual significance. We demonstrate the use of the proposed algorithm on progressive texturing geometric surfaces such as ring dupin cyclides, toruses, spheres, parametrised by rational Bezier patches. We provide experimental results to prove the effectiveness of the proposed approach, when synthesising textures of regular, irregular and stochastic nature. Further experimental results are provided to illustrate the practical use of the proposed progressive texture synthesis algorithm in resource constrained application domains. (10 pages)

Traditional fractal pattern design has some disadvantages such as inability to effectively reflect the characteristics of real scenery and texture. We propose a novel pattern design technique combining fractal geometry and image texture synthesis to solve these problems. We have improved Wei and Levoy (2000)’s texture synthesis algorithm by first using two-dimensional autocorrelation function to analyze the structure and distribution of textures, and then determining the size of L neighborhood. Several special fractal sets were adopted and HSL (Hue, Saturation, and Light) color space was chosen. The fractal structure was used to manipulate the texture synthesis in HSL color space where the pattern’s color can be adjusted conveniently. Experiments showed that patterns with different styles and different color characteristics can be more efficiently generated using the new technique.

Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.

Photographs and images often have regions which are in some sense flawed. Often, there may be a stain or an undesired feature covering a significant portion of the image, and an algorithm that can fix the image is desired. We propose a technique based on Heeger and Bergen's texture synthesis algorithm (1995). By integrating a composition step into the aforementioned algorithm one portion of the image can be substituted with a synthetic texture derived from another portion of the image. The goal of this synthetic texture is to create a plausible patch without visible seams or repetitive features. We compare this technique, which works on areas that are stochastic in nature, with other variations of image replacement which utilize a combination of image composition and texture synthesis.

In this paper we introduce a new neighborhood system based Nonparametric Markov Random Field (NMRF) model for texture synthesis. The objective of this work is to generate minimal neighborhood system, in MRF based texture synthesis algorithms. Compared to the earlier neighborhood system (circular), the proposed one is not isometric and has fewer number of pixels within the neighborhood.This reduction in the number of neighborhood pixels does not degrade the synthesis results compared to the existing neighborhood systems.Two popular texture synthesis algorithms (based on nonparametric MRF) have been tested with the new neighborhood system for two standard texture databases.The results establish the efficacy of the proposed neighborhood system in terms of computational gain, with no visible degradation compared to the earlier systems.

We propose a layered image inpainting scheme based on image decomposition. The damaged image is first decomposed into three layers: cartoon, edge, and texture. The cartoon and edge layers are repaired using an adaptive offset operator that can fill-in damaged image blocks while preserving sharpness of edges. The missing information in the texture layer is generated with a texture synthesis method. By using discrete cosine transform (DCT) in image decomposition and trading between resolution and computation complexity in texture synthesis, the processing time is kept at a reasonable level.

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.

An image inpainting technique is proposed to improve quality of photographs taken under poor weather conditions. Pale and gloomy sky is first replaced with more pleasant looking blue sky and white clouds. Transition regions near complicated boundaries are modified using a texture synthesis method that makes the sky and ground objects grow towards each other along normal directions obtained from the general trend of the boundary. This produces retouched pictures with a natural appearance

Texture synthesis and morphing are important techniques for efficiently creating realistic textures used in scientific and entertainment applications. In this paper we present a novel fast algorithm for multi-dimensional texture synthesis and morphing that is especially suitable for parallel architectures such as GPUs or direct volume rendering (DVR) hardware. Our proposed solution generalizes the synthesis process to support higher than three-dimensional synthesis and morphing.

This paper presents a novel texture synthesis algorithm that performs a sparse expansion of the patches of the image in a dictionary learned from an input exemplar. The synthesized texture is computed through the minimization of a non-convex energy that takes into account several constraints. Our first contribution is the computation of a sparse expansion of the patches imposing that the dictionary atoms are used in the same proportions as in the exemplar. This is crucial to enable a fair representation of the features of the input image during the synthesis process. Our second contribution is the use of additional penalty terms in the variational formulation to maintain the histogram and the low frequency content of the input. Lastly we introduce a non-linear reconstruction process that stitches together patches without introducing blur. Numerical results illustrate the importance of each of these contributions to achieve state of the art texture synthesis.