Conference and workshop publication<\/li>\n<\/ul>\n\n\n\n
\n\n\n\nFoundation and goals<\/h4>\n\n\n\n
The work featured in this project is heavily based on the research done by Kopf and his fellows. They generate some impressive looking 3D textures in their publication surpassing – at the time – all other contemporary efforts. Their method works with the base assumption that the inside of the texture resembles the outside to some degree. An advanced algorithm is applied which starts with a random set of samples from the original texture, and iteratively improves upon it until it matches more complete parts of the original texture. The videos below show this iterative process in action.<\/p>\n\n\n\n
A part of my Ph.D thesis concerned improving the visual quality of a real-time volumetric rendering of pig CT-scanned data. Texturing volume is significantly simplified when the applied texture is 3D, as opposed to 2D.<\/p>\n\n\n\n
\n\n\n\nAnisotropic synthesis<\/h4>\n\n\n\n
Texturing CT-scanned pig data generally requires three types of tissue to be represented: bone, meat and fat. Actual pig tissue consists of various intermediate tissue types in between meat and fat. Rather than explicitly providing more connective tissue textures, we found that blending two the meat and fat tissues to be sufficient for our needs.<\/p>\n\n\n\n
Our research concentrated on using the method devised by Kopf et al. to create anisotropic textures, since pig tissue isn’t particularly uniform. The culmination of our work was published in SCCG ’12 and can be downloaded a link above.<\/p>\n\n\n\n
\n\n\n\nAutomating detection of optimal synthesis parameters<\/h4>\n\n\n\n
Texture synthesis involves numerous parameters. We conducted a series of experiments attempting to semi-automate the detection of optimal settings for these synthesis parameters. We found that various textures would only generate the stellar results in Kopf et al.’s work when specific setting were applied. Our approach can help determine the optimal settings with number of textures, but doesn’t work reliably across the board. The work is thoroughly discussed in our internal technical report which can be downloaded via a link above.<\/p>\n\n\n\n
In an effort to improve upon the texture synthesis method, we applied the much faster PatchMatch algorithm and conducted a series of empirical studies. Unfortunately, as the videos show below, the PatchMatch algorithm results in textures that fail to properly recreate the rich diversity of the original texture. Although it might work as an intermediary step in the algorithm, substituting out the complete series of iterations results in unsatisfactory results.<\/p>\n\n\n\n
\n\n\n\nVisualized 2D texture synthesis<\/h4>\n\n\n\n