Title: View2CAD: Reconstructing View-Centric CAD Models from Single RGB-D Scans Show Chinese title

Title: View2CAD：从单个RGB-D扫描重建视点为中心的CAD模型

Abstract: 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. Show Chinese abstract

Abstract: 参数化CAD模型以边界表示法（B-rep）的形式表示，是现代设计和制造工作流的基础，提供了下游任务（如分析、编辑和制造）所需的精度和拓扑分解。然而，由于转换为更标准化但表达能力较弱的几何格式，B-rep常常无法访问。现有从测量数据恢复B-rep的方法需要完整且无噪声的3D数据，而这些数据获取起来非常耗时。我们通过从单张RGB-D图像精确重建CAD形状来缓解这一困难。我们提出了一种方法，解决从单一视角重建仅观察到的几何形状的挑战。为了允许这些部分观测，并避免产生错误的几何形状，我们引入了一种新的视点中心B-rep（VB-rep）表示法，它包含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，以细化和改进重建过程。我们的结果显示了在合成和真实RGB-D数据上的高质量重建，表明我们的方法可以弥合现实差距。