Sharp feature consolidation from raw 3D point clouds via displacement learning

Autor: Tong Zhao, Mulin Yu, Pierre Alliez, Florent Lafarge
Přispěvatelé: Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002, ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
Rok vydání: 2023
Předmět:
Zdroj: Computer Aided Geometric Design
Computer Aided Geometric Design, 2023, 13, pp.102204. ⟨10.1016/j.cagd.2023.102204⟩
ISSN: 0167-8396
Popis: International audience; Detecting sharp features in raw point clouds is an essential step in designing efficient priors in several 3D Vision applications. This paper presents a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds. We devise a multi-task neural network architecture that identifies points near sharp features and predicts displacement vectors toward the local sharp features. The so-detected points are thus consolidated via relocation. Our approach is robust against noise by utilizing a dynamic labeling oracle during the training phase. The approach is also flexible and can be combined with several popular point-based network architectures. Our experiments demonstrate that our approach outperforms the previous work in terms of detection accuracy measured on the popular ABC dataset. We show the efficacy of the proposed approach by applying it to several 3D Vision tasks.
Databáze: OpenAIRE
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