DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
This work addresses the problem of accurately identifying sharp geometric features in 3D shapes, which is crucial for applications like CAD modeling and reverse engineering, for engineers and designers.
The paper proposes Deep Estimators of Features (DEFs), a learning-based framework that regresses a scalar field representing the distance from point samples to the closest feature line on local patches to predict sharp geometric features in sampled 3D shapes. This method outperforms existing state-of-the-art methods with improvements in Recall and False Positives Rates and generalizes to real-world scans after training on synthetic data and fine-tuning on a small dataset of scanned data.
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.