DEF

نویسندگان

چکیده

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 regress scalar field representing the distance point samples closest line on local patches. Our approach is first that scales massive clouds by fusing distance-to-feature estimates obtained individual extensively evaluate our against related state-of-the-art methods newly proposed synthetic and real-world CAD model benchmarks. not only outperforms these (with improvements Recall False Positives Rates), but generalizes scans after training data fine-tuning it small dataset scanned data. demonstrate downstream application, where reconstruct an explicit representation straight curved lines range scan make code, pre-trained models, evaluation datasets available at https://github.com/artonson/def.

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ژورنال

عنوان ژورنال: ACM Transactions on Graphics

سال: 2022

ISSN: ['0730-0301', '1557-7368']

DOI: https://doi.org/10.1145/3528223.3530140