FR-CSG:快速可靠的建设性实体几何建模。

Jiaxi Chen, Zeyu Shen, Mingyang Zhao, Xiaohong Jia, Dong-Ming Yan, Wencheng Wang
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引用次数: 0

摘要

从 CAD 模型重建 CSG 树是逆向工程中的一个重要课题。尽管 CSG 重建技术取得了显著进步,但在捕捉几何细节和实现效率方面仍然存在挑战。此外,由于离散化误差导致非轴对齐的体积基元无法保持共面特性,现有的布尔运算通常会导致零体积表面,并在 CSG 建模过程中出现拓扑误差。为了解决这些问题,我们提出了一种新颖的工作流程,以实现快速 CSG 重建和可靠的正向建模。首先,我们采用特征去除和模型细分技术将模型分解为子组件。通过简化模型的复杂性,这大大加快了重建速度。然后,我们引入了更合理的基元生成和过滤方法,并利用与大小相关的优化方法重建 CSG 树。通过在 CSG 树中重新添加特征作为附加节点,我们的方法不仅保留了复杂的细节,还确保了 CSG 树的简洁性、语义完整性和可编辑性。最后,我们开发了一种共面基元离散化方法,该方法将基元表示为大平面,并提取相交后的原始三角形。我们扩展了三角形的分类,并结合了共面感知布尔树评估技术,使我们即使在极端退化的情况下,也能在没有零体积表面的情况下实现流形和不漏水的建模结果。我们证明了我们的方法优于最先进的方法。此外,我们的方法生成的重构 CSG 树包含大量语义信息,可以完成各种模型编辑任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FR-CSG: Fast and Reliable Modeling for Constructive Solid Geometry.

Reconstructing CSG trees from CAD models is a critical subject in reverse engineering. While there have been notable advancements in CSG reconstruction, challenges persist in capturing geometric details and achieving efficiency. Additionally, since non-axis-aligned volumetric primitives cannot maintain coplanar characteristics due to discretization errors, existing Boolean operations often lead to zero-volume surfaces and suffer from topological errors during the CSG modeling process. To address these issues, we propose a novel workflow to achieve fast CSG reconstruction and reliable forward modeling. First, we employ feature removal and model subdivision techniques to decompose models into sub-components. This significantly expedites the reconstruction by simplifying the complexity of the models. Then, we introduce a more reasonable method for primitive generation and filtering, and utilize a size-related optimization approach to reconstruct CSG trees. By re-adding features as additional nodes in the CSG trees, our method not only preserves intricate details but also ensures the conciseness, semantic integrity, and editability of the resulting CSG tree. Finally, we develop a coplanar primitive discretization method that represents primitives as large planes and extracts the original triangles after intersection. We extend the classification of triangles and incorporate a coplanar-aware Boolean tree assessment technique, allowing us to achieve manifold and watertight modeling results without zero-volume surfaces, even in extreme degenerate cases. We demonstrate the superiority of our method over state-of-the-art approaches. Moreover, the reconstructed CSG trees generated by our method contain extensive semantic information, enabling diverse model editing tasks.

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