Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks
This document presents a novel method based in Convolutional Neural Networks (CNN) to obtain correspondence matchings between sets of keypoints of several unorganized 3D point cloud captures, independently of the sensor used. The proposed technique extends a state-of-the-art method for correspondenc...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/132169 |
| Acceso en línea: | https://hdl.handle.net/2117/132169 https://dx.doi.org/10.1016/j.imavis.2019.02.013 |
| Access Level: | acceso abierto |
| Palabra clave: | Neural networks (Computer science) matching point cloud convolutional neural networks Xarxes neuronals (Informàtica) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | This document presents a novel method based in Convolutional Neural Networks (CNN) to obtain correspondence matchings between sets of keypoints of several unorganized 3D point cloud captures, independently of the sensor used. The proposed technique extends a state-of-the-art method for correspondence matching in standard 2D images to sets of unorganized 3D point clouds. The strategy consists in projecting the 3D neighborhood of the keypoint onto an RGBD patch, and the classi cation of patch pairs using CNNs. The objective evaluation of the proposed 3D point matching based in CNNs outperforms existing 3D feature descriptors, especially when intensity or color data is available. |
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