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.

提出了 Deep Estimators of Features（DEFs）框架，用于通过标量场回归来准确地预测采样的 3D 形状的尖锐几何特征，并使用该方法来从点云数据中恢复尖锐特征线的显式表示。