MULTI-SOURCE POINT CLOUD SEMANTIC SEGMENTATION USING NEURAL NETWORK

Autor: J. Montlahuc, A. Polette, A. Tahan, J.-P. Pernot, L. Rivest

Publikováno v: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLIII-B3-2022, Pp 515-522 (2022)

The purpose of this study is to enhance point cloud semantic segmentation by using point clouds from multiple distinct technologies on the same capture location and to determine whether employing various technologies throughout the acquisition proces

Externí odkaz: https://doaj.org/article/f9cc6984150b443ab38ec6c11ffa629b

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Fitting parametric 2D & 3D CAD models to point clouds of digitalized mechanical assemblies for reverse engineering application

Autor: G. A. Shah, J-P. Pernot, F. Giannini, M. Monti, A. Polette

Publikováno v: The European Association for Computer Graphics 40th Annual Conference-EUROGRAPHICS 2019, pp. 11–12, Genova, Italy, 6-10 May, 2019
info:cnr-pdr/source/autori:G. A. Shah, J-P. Pernot, F. Giannini, M. Monti and A. Polette/congresso_nome:The European Association for Computer Graphics 40th Annual Conference-EUROGRAPHICS 2019/congresso_luogo:Genova, Italy/congresso_data:6-10 May, 2019/anno:2019/pagina_da:11/pagina_a:12/intervallo_pagine:11–12

Following commonly used reverse engineering techniques, it is very difficult to reconstruct editable CAD parts or assemblies that can later be used and modified in the Product Development Process (PDP). Traditional methods follow a sequential timecon

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=cnr_________::6bd719124bc10dc497c665b5f5529b12
https://publications.cnr.it/doc/402985

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