SepicNet：通过三维形状中曲线的参数化推断实现锐利边缘恢复
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
AI总结：
SepicNet提出一种端到端可微的深度网络，通过参数化曲线推断和自适应点云采样，在5万三维扫描数据集上显著提升锐利边缘检测与恢复效果。
摘要：
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，被广泛应用于许多领域和行业。尽管三维扫描的质量取决于三维扫描仪的技术特性，但常见的缺陷是物体细节或边缘的平滑化。我们提出了SepicNet，这是一种新颖的深度网络，用于将三维形状中的锐利边缘检测并参数化为原始曲线。为了使网络能够端到端训练，我们以可微分的方式构建曲线拟合。我们开发了一种自适应点云采样技术，该技术比均匀采样能更好地捕捉锐利特征。实验在一个新引入的包含5万个三维扫描数据的大规模数据集上进行，其中锐利边缘标注是从其参数化CAD模型中提取的，结果表明与最先进的方法相比有显著改进。
英文摘要：
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.
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。
