3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods.

翻译：三维扫描作为一种将现实物体数字化并创建其三维模型的技术，广泛应用于众多领域。尽管三维扫描的质量取决于扫描仪的技术特性，但其常见缺陷在于对物体精细细节或边缘的平滑处理。我们提出SepicNet——一种新颖的深度网络，用于检测三维形状中的锐利边缘并将其参数化为基本曲线。为实现网络的端到端训练，我们以可微分方式构建曲线拟合过程。我们开发了一种自适应点云采样技术，该技术相比均匀采样能更有效地捕捉锐利特征。实验基于新引入的大规模数据集（包含5万个三维扫描），其锐利边缘标注从参数化CAD模型中提取，结果表明该方法相较于现有最优技术具有显著提升。