Reconstructing editable prismatic CAD from rounded voxel models
- URL: http://arxiv.org/abs/2209.01161v1
- Date: Fri, 2 Sep 2022 16:44:10 GMT
- Title: Reconstructing editable prismatic CAD from rounded voxel models
- Authors: Joseph G. Lambourne, Karl D.D. Willis, Pradeep Kumar Jayaraman,
Longfei Zhang, Aditya Sanghi, Kamal Rahimi Malekshan
- Abstract summary: We introduce a novel neural network architecture to solve this challenging task.
Our method reconstructs the input geometry in the voxel space by decomposing the shape.
During inference, we obtain the CAD data by first searching a database of 2D constrained sketches.
- Score: 16.03976415868563
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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 a
differentiable way allowing a geometric loss function to be defined. During
inference, we obtain the CAD data by first searching a database of 2D
constrained sketches to find curves which approximate the profile images, then
extrude them and use Boolean operations to build the final CAD model. Our
method approximates the target shape more closely than other methods and
outputs highly editable constrained parametric sketches which are compatible
with existing CAD software.
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