发表机构
Peking University; Shenzhen Loop Area Institute; Beijing Normal University（北京大学; 深圳河套学院; 北京师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CADForge提出一种智能体框架，通过分解物体、显式几何推理和审查反馈，将单视图图像逐步重建为可编辑的CadQuery参数化CAD模型，在保真度和感知质量上优于现有方法。
AI 中文摘要
从单视图图像重建可编辑的参数化CAD模型，对现代制造业具有重要的实用价值，但由于几何观测不完整和部件间关系复杂，这一任务仍然具有挑战性。为解决这一问题，我们提出了CADForge，一个智能体框架，可逐步将单张图像转换为CadQuery程序。CADForge将物体分解为具有CAD意义的组件，并对每个组件执行显式几何推理，该过程首先识别与CAD相关的约束，然后通过数学代码将其转化为精确的建模参数。推断出的参数随后驱动可执行CadQuery程序的组件级合成，并由一个审查智能体评估生成的几何形状，提供有针对性的反馈以进行迭代优化。为进一步提高鲁棒性和效率，CADForge引入了基于失败引导的工具库构建机制，将积累的经验提炼为工具，并维护一个紧凑的参数化CAD记忆，以便按需检索建模上下文。在多样化的单部件和多部件物体上的实验表明，CADForge在重建保真度和感知质量方面始终优于现有基线，展示了一种实现精确单视图CAD重建的有效方法。
英文摘要
Reconstructing editable parametric CAD models from a single-view image is of great practical value for modern manufacturing, yet remains challenging due to incomplete geometric observations and complex inter-part relationships. To address it, we propose CADForge, an agentic framework that progressively converts a single image into CadQuery programs. CADForge decomposes an object into CAD-meaningful components and performs explicit geometric reasoning for each component, a process that first identifies CAD-relevant constraints and then translates them into precise modeling parameters through mathematical code. The inferred parameters then drive component-wise synthesis of executable CadQuery programs, with a review agent evaluating the resulting geometry and providing targeted feedback for iterative refinement. To further improve robustness and efficiency, CADForge incorporates a failure-guided toolkit construction mechanism to distill accumulated experience into tools, and maintains a compact parametric CAD memory for retrieving modeling context on demand. Experiments on diverse single- and multi-part objects show that CADForge consistently outperforms existing baselines in reconstruction fidelity and perceptual quality, demonstrating an effective approach to accurate single-view CAD reconstruction.
