Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/69217
Título: | Comparison of major LiDAR data-driven feature extraction methods for autonomous vehicles |
Autor(es): | Fernandes, Duarte Manuel Azevedo Névoa, Rafael Silva, António José Linhares Simões, Cláudia Monteiro, João L. Novais, Paulo Melo, Pedro |
Palavras-chave: | 3D Object Detection and Classification CNNs LiDAR Point clouds |
Data: | 1-Jan-2020 |
Editora: | Springer |
Revista: | Advances in Intelligent Systems and Computing |
Citação: | Fernandes, D., Névoa, R., Silva, A., Simões, C., Monteiro, J., Novais, P., & Melo, P. (2020, April). Comparison of Major LiDAR Data-Driven Feature Extraction Methods for Autonomous Vehicles. In World Conference on Information Systems and Technologies (pp. 574-583). Springer |
Resumo(s): | Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/69217 |
ISBN: | 978-3-030-45690-0 |
e-ISBN: | 978-3-030-45691-7 |
DOI: | 10.1007/978-3-030-45691-7_54 |
ISSN: | 2194-5357 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-45691-7_54 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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WorldCIST_20___Comparison_of_major_LiDAR_data_driven_feature_extraction_methods_for_autonomous_vehicles.pdf | 3,92 MB | Adobe PDF | Ver/Abrir |