Reconstruction of Euclidean planes from voxels

T. Linh, A. Imiya
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引用次数: 1

Abstract

We aim to formulate the recognition of a planes from a discrete point set as a nonlinear optimization problem, and we prove a uniqueness theorem for the solution of this problem. We deal with the supercover model in a space for the expression of discrete planes. The algorithm achieves invertible data compression of digital objects, since the algorithm transforms a collection voxels to a collection of plane parameters, which classify the voxels.
从体素重建欧几里得平面
本文将离散点集的平面识别问题表述为一个非线性优化问题,并证明了该问题解的唯一性定理。我们处理空间中离散平面的超覆盖模型。该算法通过将一组体素变换为一组平面参数,对体素进行分类,实现了数字对象的可逆数据压缩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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