从大型含噪点云中对薄叶片表面的隐式重建
Implicit reconstructions of thin leaf surfaces from large, noisy point clouds
AI总结：
本文提出一种结合多调和光滑样条与单位分解的隐式重建方法，可在O(N)复杂度下从大型含噪点云中重建可定向薄叶片表面，并成功应用于辣椒和番茄植株的液滴铺展模拟。
摘要：
薄表面（例如植物叶片）对隐式表面重建技术构成重大挑战，因为这类技术通常假设表面是封闭且可定向的。我们证明，通过对表面点云（并补充离面点）进行近似插值，并将插值函数的求值限制在点云周围的紧致域内，重建时只需表面可定向即可。我们使用多调和光滑样条（polyharmonic smoothing splines）对含噪数据进行近似插值拟合，并采用具有类八叉树（octree-like）子域选择策略的单位分解（partition of unity）方法。该方法使我们能够在 O(N) 次运算中插值包含 N 个点的数据集。我们展示了使用手持设备扫描的辣椒和番茄植株点云的重建结果。本工作的一项重要成果是生成了足够光滑的叶片表面，可用于液滴铺展模拟。
英文摘要：
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.
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