High quality solid texture synthesis using position and index histogram matching

International audienceThe synthesis quality is one of the most important aspects in solid texture synthesis algorithms. In recent years several methods are proposed to generate high quality solid textures. However, these existing methods often suffer from the synthesis artifacts such as blurring, missing texture structures, introducing aberrant voxel colors, and so on. In this paper, we introduce a novel algorithm for synthesizing high quality solid textures from 2D exemplars. We first analyze the relevant factors for further improvements of the synthesis quality, and then adopt an optimization framework with the k-coherence search and the discrete solver for solid texture synthesis. The texture optimization approach is integrated with two new kinds of histogram matching methods, position and index histogram matching, which effectively cause the global statistics of the synthesized solid textures to match those of the exemplars. Experimental results show that our algorithm outperforms or at least is comparable to the previous solid texture synthesis algorithms in terms of the synthesis quality

Non-parametric synthesis of laminar volumetric texture

International audienceThe goal of this paper is to evaluate several extensions of Wei and Levoy's algorithm for the synthesis of laminar volumetric textures constrained only by a single 2D sample. Hence, we shall also review in a unified form the improved algorithm proposed by Kopf et al. and the particular histogram matching approach of Chen and Wang. Developing a genuine quantitative study we are able to compare the performances of these algorithms that we have applied to the synthesis of volumetric structures of dense carbons. The 2D samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM)

Solid Texture Synthesis using Generative Adversarial Networks

Solid texture synthesis, as an effective way to extend 2D exemplar to a volumetric texture, exhibits advantages in numerous application domains. However, existing methods generally suffer from synthesis distortion due to the under-utilization of information. In this paper, we propose a novel approach for the solid texture synthesis based on generative adversarial networks(GANs), named STS-GAN, learning the distribution of 2D exemplars with volumetric operation in a feature-free manner. The multi-scale discriminators evaluate the similarities between patch exemplars and slices from generated volume, promoting the generator to synthesize realistic solid texture. Experimental results demonstrate that the proposed method can synthesize high-quality solid texture with similar visual characteristics to the exemplar

Patient-specific anatomical illustration via model-guided texture synthesis

Medical illustrations can make powerful use of textures to attractively, effectively, and efficiently visualize the appearance of the surface or cut surface of anatomic structures. It can do this by implying the anatomic structure's physical composition and clarifying its identity and 3-D shape. Current visualization methods are only capable of conveying detailed information about the orientation, internal structure, and other local properties of the anatomical objects for a typical individual, not for a particular patient. Although one can derive the shape of the individual patient's object from CT or MRI, it is important to apply these illustrative techniques to those particular shapes. In this research patient-specific anatomical illustrations are created by model-guided texture synthesis (MGTS). Given 2D exemplar textures and model-based guidance information as input, MGTS uses exemplar-based texture synthesis techniques to create patient-specific surface and solid textures. It consists of three main components. The first component includes a novel texture metamorphosis approach for creating interpolated exemplar textures given two exemplar textures. This component uses an energy optimization scheme derived from optimal control principles that utilizes intensity and structure information in obtaining the transformation. The second component consists of creating the model-based guidance information, such as directions and layers, for that specific model. This component uses coordinates implied by discrete medial 3D anatomical models (m-reps). The last component accomplishes exemplar-based texture synthesis by textures whose characteristics are spatially variant on and inside the 3D models. It considers the exemplar textures from the first component and guidance information from the second component in synthesizing high-quality, high-resolution solid and surface textures. Patient-specific illustrations with a variety of textures for different anatomical models, such as muscles and bones, are shown to be useful for our clinician to comprehend the shape of the models under radiation dose and to distinguish the models from one another

User-appropriate viewer for high resolution interactive engagement with 3D digital cultural artefacts.

The core mission of museums and cultural institutions is the preservation, study and presentation of cultural heritage content. In this technological age, the creation of digital datasets and archives has been widely adopted as one way of seeking to achieve some or all of these goals. However, there are many challenges with the use of these data, and in particular the large numbers of 3D digital artefacts that have been produced using methods such as non- contact laser scanning. As public expectation for more open access to information and innovative digital media increases, there are many issues that need to be rapidly addressed. The novel nature of 3D datasets and their visualisation presenting unique issues that impede use and dissemination. Key questions include the legal issues associated with 3D datasets created from cultural artefacts; the complex needs of users who are interacting with them; a lack of knowledge to texture and assess the visual quality of the datasets; and how the visual quality of the presented dataset relates to the perceptual experience of the user. This engineering doctorate, based on an industrial partnership with the National Museums of Liverpool and Conservation Technologies, investigates these questions and offers new ways of working with 3D cultural heritage datasets. The research outcomes in the thesis provide an improved understanding of the complexity of intellectual property law in relation to 3D cultural heritage datasets and how this impacts dissemination of these types of data. It also provides tools and techniques that can be used to understand the needs of a user when interacting with 3D cultural content. Additionally, the results demonstrate the importance of the relationship between texture and polygonal resolution and how this can affect the perceived visual experience of a visitor. It finds that there is an acceptable cost to texture and polygonal resolution to offer the best perceptual experience with 3D digital cultural heritage. The results also demonstrate that a non-textured mesh may be as highly received as a high resolution textured mesh. The research presented provides methodologies and guidelines to improve upon the dissemination and visualisation of 3D cultural content; enhancing and communicating the significance of their 3D collections to their physical and virtual visitors. Future opportunities and challenges for disseminating and visualising 3D cultural content are also discussed