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.
@article{arxiv.2011.15081,
title = {DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes},
author = {Albert Matveev and Ruslan Rakhimov and Alexey Artemov and Gleb Bobrovskikh and Vage Egiazarian and Emil Bogomolov and Daniele Panozzo and Denis Zorin and Evgeny Burnaev},
journal= {arXiv preprint arXiv:2011.15081},
year = {2022}
}