从三角形网格稳健地生成符号距离域

J. Baerentzen
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引用次数: 17

摘要

提出了一种从三角形网格中鲁棒生成距离场的新方法。图形硬件用于加速生成分层深度图像的技术。从多层深度图像中提取二值体和点表示。然后使用点信息将二进制体转换为距离场。该方法鲁棒性好,可以处理孔洞、伪三角形和歧义。此外,该方法还适用于实体之间的布尔运算。由于生成了点云和带符号的距离,因此可以提取距离场的等值面并将其拟合到点集。使用这种方法,可以恢复尖锐的边缘信息。给出了用距离场生成方法结合网格拟合对三角形网格进行布尔运算和形态运算的实例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust generation of signed distance fields from triangle meshes
A new method for robust generation of distance fields from triangle meshes is presented. Graphics hardware is used to accelerate a technique for generating layered depth images. From multiple layered depth images, a binary volume and a point representation are extracted. The point information is then used to convert the binary volume into a distance field. The method is robust and handles holes, spurious triangles and ambiguities. Moreover, the method lends itself to Boolean operations between solids. Since a point cloud as well as a signed distance is generated, it is possible to extract an iso-surface of the distance field and fit it to the point set. Using this method, one may recover sharp edge information. Examples are given where the method for generating distance fields coupled with mesh fitting is used to perform Boolean and morphological operations on triangle meshes.
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