DEF：三维曲面中尖锐几何特征的深度估计
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
摘要：
我们提出了深度特征估计器（Deep Estimators of Features，DEF），这是一个基于学习的框架，用于预测采样的3D形状中的尖锐几何特征。从现有的数据驱动的方法，这减少了这个问题的特征分类，我们建议回归一个标量场表示从点样本到最近的特征线上的局部补丁的距离。我们的方法是第一个通过融合单个补丁上获得的距离到特征估计来扩展到大规模点云的方法。我们广泛评估我们的方法对相关国家的最先进的方法，新提出的合成和现实世界的3D CAD模型基准。我们的方法不仅优于这些方法（在召回率和假阳性率方面有所改进），而且在合成数据上训练我们的模型并在扫描数据的小数据集上对其进行微调后，可以推广到现实世界的扫描。我们展示了一个下游应用程序，在那里我们重建一个明确的表示，直和弯曲的尖锐的特征线范围扫描数据。
英文摘要：
We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven methods, which reduce this problem to feature classification, we propose to regress a scalar field representing the distance from point samples to the closest feature line on local patches. Our approach is the first that scales to massive point clouds by fusing distance-to-feature estimates obtained on individual patches. We extensively evaluate our approach against related state-of-the-art methods on newly proposed synthetic and real-world 3D CAD model benchmarks. Our approach not only outperforms these (with improvements in Recall and False Positives Rates), but generalizes to real-world scans after training our model on synthetic data and fine-tuning it on a small dataset of scanned data. We demonstrate a downstream application, where we reconstruct an explicit representation of straight and curved sharp feature lines from range scan data.
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