VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding

2026-09-17

AI Summary

本文提出VGGT-CAD，通过几何先验和多视图特征融合解决参数化CAD模型从有限视角重建的问题。

Abstract

Parametric CAD reconstruction requires recovering both precise geometry and editable modeling operations from visual observations, making it challenging under limited and ambiguous views. Existing methods mainly rely on 2D appearance cues and lack strong multi-view geometric priors. In this work, we present VGGT-CAD, a geometry-aware framework for parametric CAD reconstruction from single- and multi-view observations. We transfer pretrained 3D geometric priors into CAD reconstruction by encoding camera parameters as condition tokens and jointly modeling them with image tokens. To handle varying numbers of viewpoints, we introduce a variable-view cross-view context aggregation module that adaptively fuses multi-view features. We further develop a training-free geometry-aware view selection strategy to select complementary and reliable frames during inference. The resulting representation is decoded into CAD command sequences using a non-autoregressive decoder. We also develop VideoCAD, a large-scale multi-view video benchmark derived from existing CAD data through multi-view re-rendering. Extensive experiments demonstrate the effectiveness of VGGT-CAD for visual CAD reconstruction under different observation configurations.

Problem

• parametric CAD
• reconstruction
• visual observations
• geometric priors
• multi-view

Innovation

• geometry-aware framework
• variable-view cross-view context aggregation
• non-autoregressive decoder
• pretrained 3D geometric priors