Colorization in Gabor Space and Realistic Surface Rendering on GPUs

Texture feature similarity has long been crucial and important topic in VR/graphics applications, such as image and video colorizations, surface texture synthesis and geometry image applications. Generally, the image feature is highly subjective, depending on not only the image pixels but also interactive users. Existing colorization and surface texture synthesis pay little attention to the generation of conforming color/textures that accurately reflect exemplar structures or user's intension. Realistic surface synthesis remains a challenging task in VR/graphics researches. In this dissertation, we focus on the encoding of the Gabor filter banks into texture feature similarity computations and GPU-parallel surface rendering faithfully, including image/vodeo colorizations, parallel texturing of geometric surfaces, and multiresolution rendering on sole-cube maps (SCMs).;Based on the construction of Gabor feature space, which is important in applying pixel similarity computations, we formalize the space using rotation-invariant Gabor filter banks and apply optimizations in texture feature space. In image colorizations, the pixels that have similar Gabor features appear similar colors, our approach can colorize natural images globally, without the restriction of the disjoint regions with similar texture-like appearances. Our approach supports the two-pass colorization processes: coloring optimization in Gabor space and color detailing for progressive effects. We further work on the video colorization using the optimized Gabor flow computing, including coloring keyframes, color propagation by Gabor filtering, and optimized parallel computing over the video. Our video colorization is designed in a spatiotemporal manner to keep temporal coherence, and provides simple closed-form solutions in energy optimization that yield fast colonizations. Moreover, we develop parallel surface texturing of geometric models on GPU, generating spatially-varying visual appearances. We incorporate the Gabor feature space for the searching of 2D exemplars, to determine the k-coherence candidate pixels. The multi-pass correction in synthesis is applied to the local neighborhood for parallel processes. The iso/aniso-scale texture synthesis leverages the strengths of GPU computing, so to synthesize the iso/aniso-scale texturing appearance in parallel over arbitrary surfaces. Our experimental results showed that our approach produces simply controllable texturing effects of surface synthesis, generating texture-similar and spatially-varying visual appearances with GPU accelerated performance.;We further explore the GPU-based multiresolution rendering on solecube maps (SCMs). Our SCMs on GPU generate adaptive mesh surfaces dynamically, and are fully developed in parallelization for large-scale and complex VR environments. We also encapsulate the differential coordinates in SCMs, reflecting the local geometric characteristics for geometric modeling and interactive animation applications. For the future work, we will work on improving the image/ video feature analysis framework in VR/graphics applications. The further work lying in the surface texture synthesis includes the interactive control of texture orientations by surface vector fields using sketch editing, so to widen the gamut of interactive tools available for texturing artists and end users.