Browsing by Subject "Graphics"
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Item Automated texture mapping of laser based range images(Texas Tech University, 2005-08) Srisinroongruang, Rattasak; Sinzinger, Eric D.; Hernandez, Hector J.; Lakhani, GopalTexture mapping is the process of applying a 2D image onto a 3D planar surface. This requires the generation of a mapping that defines the relationship between the 2D coordinates of the image and the 3D coordinates of the surface. The goal of this research is to provide a method of automatically generating this mapping given a 3D object at arbitrary orientation and a 2D image that may contain unwanted background information. A review of the current methods of texture mapping, image segmentation, and basic 3D viewing transforms is given. An algorithm to compute this alignment given the proper segmentation of the 2D image is then proposed and tested with five different models. The results of the generated alignment and mapping are then discussed, showing the level of accuracy of the final texture mapped model.Item Automated texture mapping of laser based range images(2005-08) Srisinroongruang, Rattasak; Sinzinger, Eric D.; Hernandez, Hector J.; Lakhani, GopalTexture mapping is the process of applying a 2D image onto a 3D planar surface. This requires the generation of a mapping that defines the relationship between the 2D coordinates of the image and the 3D coordinates of the surface. The goal of this research is to provide a method of automatically generating this mapping given a 3D object at arbitrary orientation and a 2D image that may contain unwanted background information. A review of the current methods of texture mapping, image segmentation, and basic 3D viewing transforms is given. An algorithm to compute this alignment given the proper segmentation of the 2D image is then proposed and tested with five different models. The results of the generated alignment and mapping are then discussed, showing the level of accuracy of the final texture mapped model.Item Intuitive Generation of Realistic Motions for Articulated Human Characters(2013-01-15) Min, JianyuanA long-standing goal in computer graphics is to create and control realistic motion for virtual human characters. Despite the progress made over the last decade, it remains challenging to design a system that allows a random user to intuitively create and control life-like human motions. This dissertation focuses on exploring theory, algorithms and applications that enable novice users to quickly and easily create and control natural-looking motions, including both full-body movement and hand articulations, for human characters. More specifically, the goals of this research are: (1) to investigate generative statistical models and physics-based dynamic models to precisely predict how humans move and (2) to demonstrate the utility of our motion models in a wide range of applications including motion analysis, synthesis, editing and acquisition. We have developed two novel generative statistical models from prerecorded motion data and show their promising applications in real time motion editing, online motion control, offline animation design, and motion data processing. In addition, we have explored how to model subtle contact phenomena for dexterous hand grasping and manipulation using physics-based dynamic models. We show for the first time how to capture physically realistic hand manipulation data from ambiguous image data obtained by video cameras.Item Performance-efficient mechanisms for managing irregularity in throughput processors(2014-05) Rhu, Minsoo; Erez, MattanRecent graphics processing units (GPUs) have emerged as a promising platform for general purpose computing and have been shown to be very efficient in executing parallel applications with regular control and memory access behavior. Current GPU architectures primarily adopt the single-instruction multiple-thread (SIMT) programming model that balances programmability and hardware efficiency. With SIMT, the programmer writes application code to be executed by scalar threads and each thread is supported with conditional branch and fine-grained load/store instruction for ease of programming. At the same time, the hardware and software collaboratively enable the grouping of scalar threads to be executed in a vectorized single-instruction multiple-data (SIMD) in-order pipeline, simplifying hardware design. As GPUs gain momentum in being utilized in various application domains, these throughput processors will increasingly demand more efficient execution of irregular applications. Current GPUs, however, suffer from reduced thread-level parallelism, underutilization of compute resources, inefficient on-chip caching, and waste in off-chip memory bandwidth utilization for highly irregular programs with divergent control and memory accesses. In this dissertation, I develop techniques that enable simple, robust, and highly effective performance optimizations for SIMT-based throughput processor architectures such that they can better manage irregularity. I first identify that previously suggested optimizations to the divergent control flow problem suffers from the following limitations: 1) serialized execution of diverging paths, 2) lack of robustness across regular/irregular codes, and 3) limited applicability. Based on such observations, I propose and evaluate three novel mechanisms that resolve the aforementioned issues, providing significant performance improvements while minimizing implementation overhead. In the second half of the dissertation, I observe that conventional coarse-grained memory hierarchy designs do not take into account the massively multi-threaded nature of GPUs, which leads to substantial waste in off-chip memory bandwidth utilization. I design and evaluate a locality-aware memory hierarchy for throughput processors, which retains the advantages of coarse-grained accesses for spatially and temporally local programs while permitting selective fine-grained access to memory. By adaptively adjusting the access granularity, memory bandwidth and energy consumption are reduced for data with low spatial/temporal locality without wasting control overheads or prefetching potential for data with high spatial locality.Item Smoothing Wavelet Reconstruction(2013-04-23) Garg, DeepakThis thesis present a new algorithm for creating high quality surfaces from large data sets of oriented points, sampled using a laser range scanner. This method works in two phases. In the first phase, using wavelet surface reconstruction method, we calculate a rough estimate of the surface in the form of Haar wavelet coefficients, stored in an Octree. In the second phase, we modify these coefficients to obtain a higher quality surface. We cast this method as a gradient minimization problem in the wavelet domain. We show that the solution to the gradient minimization problem, in the wavelet domain, is a sparse linear system with dimensionality roughly proportional to the surface of the model in question. We introduce a fast inplace method, which uses various properties of Haar wavelets, to solve the linear system and demonstrate the results of the algorithm.