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Reconstructing editable prismatic CAD from rounded voxel models. (arXiv:2209.01161v1 [cs.CV])
Sept. 5, 2022, 1:12 a.m. | Joseph G. Lambourne, Karl D.D. Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, Kamal Rahimi Malekshan
cs.LG updates on arXiv.org arxiv.org
Reverse Engineering a CAD shape from other representations is an important
geometric processing step for many downstream applications. In this work, we
introduce a novel neural network architecture to solve this challenging task
and approximate a smoothed signed distance function with an editable,
constrained, prismatic CAD model. During training, our method reconstructs the
input geometry in the voxel space by decomposing the shape into a series of 2D
profile images and 1D envelope functions. These can then be recombined in …
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