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Implicit reconstructions of thin leaf surfaces from large, noisy point clouds
Applied Mathematical Modelling ( IF 4.4 ) Pub Date : 2021-06-02 , DOI: 10.1016/j.apm.2021.05.014
Riley M. Whebell , Timothy J. Moroney , Ian W. Turner , Ravindra Pethiyagoda , Scott W. McCue

Thin surfaces, such as the leaves of a plant, pose a significant challenge for implicit surface reconstruction techniques, which typically assume a closed, orientable surface. We show that by approximately interpolating a point cloud of the surface (augmented with off-surface points) and restricting the evaluation of the interpolant to a tight domain around the point cloud, we need only require an orientable surface for the reconstruction. We use polyharmonic smoothing splines to fit approximate interpolants to noisy data, and a partition of unity method with an octree-like strategy for choosing subdomains. This method enables us to interpolate an N-point dataset in O(N) operations. We present results for point clouds of capsicum and tomato plants, scanned with a handheld device. An important outcome of the work is that sufficiently smooth leaf surfaces are generated that are amenable for droplet spreading simulations.



中文翻译:

从大的、嘈杂的点云隐式重建薄叶表面

薄表面(例如植物的叶子)对隐式表面重建技术提出了重大挑战,这些技术通常假定为封闭的、可定向的表面。我们表明,通过对表面的点云进行近似插值(用表面外点增强)并将插值的评估限制在点云周围的紧密域中,我们只需要一个可定向的表面来进行重建。我们使用多谐平滑样条来拟合噪声数据的近似插值,以及使用类似八叉树的策略来选择子域的统一方法的分区。这种方法使我们能够插入一个N- 点数据集在 (N)操作。我们展示了用手持设备扫描的辣椒和番茄植物点云的结果。这项工作的一个重要成果是生成了足够光滑的叶片表面,可用于液滴扩散模拟。

更新日期:2021-06-18
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