Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud

2026-05-09

AI Summary

This work addresses the challenge of efficiently reconstructing structured and editable CAD models from unorganized point clouds by proposing an end-to-end deep learning framework. The method innovatively introduces an extrusion-based segmentation strategy that decomposes input point clouds into individual extrusion primitives. This approach significantly enhances data diversity and reconstruction robustness without increasing model complexity. Experimental results demonstrate that the proposed framework substantially improves both the accuracy and generalization capability of CAD model reconstruction, offering a more reliable automated solution for applications in reverse engineering and manufacturing quality control.

Abstract

Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time, advances in 3D sensing have made point clouds a dominant form of raw 3D data. Recovering the CAD model of a physical object from its point cloud scan has two major applications: reverse engineering, where physical or hand-crafted prototypes need to be reconstructed automatically as editable digital models, and quality control, where recovering the CAD description of a manufactured object helps quantify and understand deviations introduced during the production process. Thus, converting unordered point clouds into structured CAD models is increasingly important for modern applications. Deep learning has enabled major progress in computer vision for both 2D and 3D data, and new datasets facilitate data-driven CAD reconstruction. Building on this foundation, we develop an end-to-end model that reconstructs CAD models from point clouds and introduce a segmentation approach that decomposes them into individual extrusions. These partial shapes increase data diversity, improving the generalization and robustness of deep learning models. Our strategy thereby provides a simple, yet effective way to increase reconstruction performance of deep learning models.

Problem

• CAD reconstruction
• point cloud
• extrusion segmentation
• reverse engineering
• quality control

Innovation

• Extrusion Segmentation
• CAD Reconstruction
• Point Cloud
• Deep Learning
• Reverse Engineering