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GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting by Zhou, Zheng; Li, Zhe; Yu, Bo; Hu, Lina; Dong, Liang; Yang, Zijian; Liu, Xiaoli; Xu, Ning; Wang, Ziwei; Dang, Yonghao; Yin, Jianqin is a scholarly article available to read on EtoBox.

What is GaussianCAD: Robust Self-Supervised CAD Reconstruction from Three Orthographic Views Using 3D Gaussian Splatting about?

The automatic reconstruction of 3D computer-aided design (CAD) models from CAD sketches has recently gained significant attention in the computer vision community. Most existing methods, however, rely on vector CAD sketches and 3D ground truth for supervision, which are often difficult to be obtained in industrial applications and are sensitive to noise inputs. We propose viewing CAD reconstruction as a specific instance of sparse-view 3D reconstruction to overcome these limitations. While this reformulation offers a promising perspective, existing 3D reconstruction methods typically require natural images and corresponding camera poses as inputs, which introduces two major significant challenges: (1) modality discrepancy between CAD sketches and natural images, and (2) difficulty of accurate camera pose estimation for CAD sketches. To solve these issues, we first transform the CAD sketches into representations resembling natural images and extract corresponding masks. Next, we manually calculate the camera poses for the orthographic views to ensure accurate alignment within the 3D coordinate system. Finally, we employ a customized sparse-view 3D reconstruction method to achieve hi

Author

Zhou, Zheng; Li, Zhe; Yu, Bo; Hu, Lina; Dong, Liang; Yang, Zijian; Liu, Xiaoli; Xu, Ning; Wang, Ziwei; Dang, Yonghao; Yin, Jianqin

Published

2025

Language

EN