UniBRep：面向图像条件B-rep生成的统一几何与拓扑学习
UniBRep: Learning Unified Geometry and Topology for Image-conditioned B-Rep Generation
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中文总结 AI 辅助
UniBRep通过几何优先框架和特征网格统一表示，从单张图像生成B-rep，在DeepCAD基准上实现80.49%有效率和更低的面Chamfer距离，并支持复杂形状。
中文摘要 AI 辅助
基于单张图像生成边界表示（B-rep）需要忠实的几何重建、有效的拓扑结构以及对复杂形状的支持。我们提出了UniBRep，一个几何优先的框架，它适配预训练的图像到3D模型，生成特征网格作为统一的中间表示。其表面提供了几何支架，而空间对齐的学习特征编码了用于拓扑恢复的面分离线索。双解码器分支生成几何和面分离特征；随后，一个几何与特征引导的构建流程拟合参数曲面，恢复边界曲线和连接性，并使用CAD内核组装显式B-rep。从网格区域恢复拓扑避免了预定义的架构面数限制，使得面数能够随形状复杂度扩展。在标准DeepCAD基准上，UniBRep对80.49%的输入生成了有效的B-rep，并将面Chamfer距离从0.1096降至0.0345（相对于CADDreamer）。在匹配比较中，UniBRep在所有报告的指标上也优于HoLa公开演示。进一步的评估展示了其可扩展性，能够处理超出标准30面范围的高复杂度形状，对CAD训练分布之外物体的泛化能力，以及对真实照片的定性迁移。
英文摘要
Generating a boundary representation (B-rep) conditioned on a single image requires faithful reconstruction of geometry, valid topology, and support for complex shapes. We present UniBRep, a geometry-first framework that adapts a pretrained image-to-3D model to generate a feature mesh as a unified intermediate representation. Its surface provides a geometric scaffold, while spatially aligned learned features encode face-separation cues for topology recovery. Dual decoder branches generate the geometry and face-separation features; a geometry- and feature-guided construction pipeline then fits parametric surfaces, recovers boundary curves and connectivity, and assembles an explicit B-rep using a CAD kernel. Recovering topology from mesh regions avoids predefined architectural face-count limits, allowing face count to scale with shape complexity. On the standard DeepCAD benchmark, UniBRep produces valid B-reps for 80.49\% of inputs and reduces face Chamfer distance from 0.1096 to 0.0345 relative to CADDreamer. In a matched comparison, UniBRep also outperforms the HoLa public demo across all reported metrics. Further evaluations demonstrate scalability to high-complexity shapes beyond the standard 30-face range, generalization to objects outside the CAD training distribution, and qualitative transfer to real photographs.
发表机构
- University of Maryland, College Park（马里兰大学学院公园分校）
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
