Browsing by Author "Altenhofen, Christian"

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    Direct Limit Volumes: Constant-Time Limit Evaluation for Catmull-Clark Solids
    (The Eurographics Association, 2018) Altenhofen, Christian; Müller, Joel; Weber, Daniel; Stork, André; Fellner, Dieter W.; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    We present a novel approach for efficient limit volume evaluation on Catmull-Clark (CC) subdivision solids. Although several analogies exist between subdivision surfaces and subdivision volumes, extending Stam's limit evaluation technique from 2 to 3 dimensions is not straightforward, as irregularities and boundaries introduce new challenges in the volumetric case. We present new direct evaluation techniques for irregular volumetric topologies and boundary cells, which allow for calculating the limit of CC subdivision solids at arbitrary parameter values in constant time. Evaluation of limit points is a central aspect when using CC solids for applications such as simulation and multi-material additive manufacturing, or as a compact volumetric representation scheme for continuous scalar fields. We demonstrate that our approach is faster than existing evaluation techniques for every topological configuration or target parameter (u, v, w) that requires more than two local subdivision steps.
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    GPU-Parallel Constant-Time Limit Evaluation of Catmull-Clark Solids
    (The Eurographics Association, 2021) Besler, Sebastian; Altenhofen, Christian; Stork, André; Fellner, Dieter W.; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Subdivision solids, such as Catmull-Clark (CC) solids, are versatile volumetric representation schemes that can be employed for geometric modeling, physically based simulation, and multi-material additive manufacturing. With volumetric limit evaluation still being the performance bottleneck for these applications, we present a massively parallel approach to Altenhofen et al.'s constant-time limit evaluation method for CC solids. Our algorithm exploits the computational power of modern GPUs, while maintaining the mathematical concepts of Altenhofen et al.'s method. Distributing the computations for a single cell across multiple streaming multiprocessors (SMs) increases the utilization of the GPU's resources compared to straightforward parallelization. Specialized compute kernels for different topological configurations optimize shared memory usage and memory access. Our hybrid approach dynamically chooses the best kernel based on the topology and the evaluation parameters, resulting in speedups of between 5.75x and 61.58x compared to a CPU-parallel implementation of Altenhofen et al.'s method.