Reconstructing editable prismatic CAD from rounded voxel models

TL;DR

This paper presents a neural network-based method for reconstructing editable, constrained prismatic CAD models from rounded voxel shapes, enabling more accurate and parametric CAD reverse engineering.

Contribution

Introduces a novel neural architecture that reconstructs editable CAD models from voxel data using a differentiable process and database search for sketches.

Findings

More accurate shape approximation than existing methods

Produces highly editable constrained parametric sketches

Compatible with standard CAD software

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.