View2CAD：从单次 RGB-D 扫描重建以视图为中心的 CAD 模型
View2CAD: Reconstructing View-Centric CAD Models from Single RGB-D Scans
AI总结：
提出从单张RGB-D图像精确重建CAD形状的方法，引入以视图为中心的VB-Rep表示处理可见性与几何不确定性，结合全景分割与迭代几何优化，在合成与真实数据上实现高质量重建并弥合现实差距。
摘要：
表示为边界表示（B-reps）的参数化 CAD 模型是现代设计和制造工作流程的基础，为分析、编辑和制造等下游任务提供了所需的精度和拓扑分解。然而，由于转换为更标准化、表达能力较弱的几何格式，B-Reps 通常难以获取。现有从测量数据恢复 B-Reps 的方法需要完整、无噪声的 3D 数据，这些数据获取起来非常费力。我们通过实现从单张 RGB-D 图像精确重建 CAD 形状来缓解这一困难。我们提出了一种解决仅从单一视图重建观测到的几何形状这一挑战的方法。为了允许这些部分观测，并避免幻觉出不正确的几何形状，我们引入了一种新颖的以视图为中心的 B-rep（VB-Rep）表示，它结合了处理可见性限制和编码几何不确定性的结构。我们将全景图像分割与迭代几何优化相结合，以改进和提升重建过程。我们的结果在合成和真实 RGB-D 数据上展示了高质量的重建，表明我们的方法能够弥合现实差距。
英文摘要：
Parametric CAD models, represented as Boundary Representations (B-reps), are foundational to modern design and manufacturing workflows, offering the precision and topological breakdown required for downstream tasks such as analysis, editing, and fabrication. However, B-Reps are often inaccessible due to conversion to more standardized, less expressive geometry formats. Existing methods to recover B-Reps from measured data require complete, noise-free 3D data, which are laborious to obtain. We alleviate this difficulty by enabling the precise reconstruction of CAD shapes from a single RGB-D image. We propose a method that addresses the challenge of reconstructing only the observed geometry from a single view. To allow for these partial observations, and to avoid hallucinating incorrect geometry, we introduce a novel view-centric B-rep (VB-Rep) representation, which incorporates structures to handle visibility limits and encode geometric uncertainty. We combine panoptic image segmentation with iterative geometric optimization to refine and improve the reconstruction process. Our results demonstrate high-quality reconstruction on synthetic and real RGB-D data, showing that our method can bridge the reality gap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
