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arXiv · 2009.10286

Implicit reconstructions of thin leaf surfaces from large, noisy point clouds

Riley M. Whebell·Timothy J. Moroney·Ian W. Turner·Ravindra Pethiyagoda·Scott W. McCue
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Abstract

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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Riley M. Whebell, Timothy J. Moroney, Ian W. Turner, Ravindra Pethiyagoda, Scott W. McCue. 2020-09-22. Implicit reconstructions of thin leaf surfaces from large, noisy point clouds. https://doi.org/10.1016/j.apm.2021.05.014

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