基于gpgpu的高并行三维节点定位技术用于实时三维模型再现

Proceedings of the 22nd International Conference on Intelligent User Interfaces Pub Date : 2017-03-07 DOI:10.1145/3025171.3025183

Kauzki Hirosue, Shohei Ukawa, Yuichi Itoh, T. Onoye, M. Hashimoto

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引用次数: 3

摘要

针对三维建模系统，提出了一种基于交叉熵法的高度并行化三维节点定位方法。交叉熵定位通过采样从节点到节点的距离信息中统计估计节点位置，每个样本的评估和目标函数的内部计算可以并行进行。实验结果表明，与单处理器和80处理器实现相比，基于gpgpu的实现速度分别提高了5163倍和61.5倍。此外，为了提高模型再现的准确性，本工作引入了一个惩罚函数来减轻翻转模糊。

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GPGPU-based Highly Parallelized 3D Node Localization for Real-Time 3D Model Reproduction

This paper proposes a highly parallelized 3D node localization method based on cross-entropy method for the 3D modeling system. Cross-entropy localization statistically estimates node positions from node-to-node distance information by sampling, and each sample evaluation and internal computation of objective function can be processed in parallel. Experimental results show our GPGPU-based implementation achieved 5,163x and 61.5x speed up compared to a single processor and 80-processor implementations. In addition, for enhancing model reproduction accuracy, this work introduces a penalty function to mitigate flip ambiguity.

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Proceedings of the 22nd International Conference on Intelligent User Interfaces

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