Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification

Abstract : This article presents a dataset called Paris-Lille-3D. This dataset is composed of several point clouds of outdoor scenes in Paris and Lille, France, with a total of more than 140 million hand labeled and classified points with more than 50 classes (e.g., the ground, cars and benches). This dataset is large enough and of high enough quality to further research on techniques regarding the automatic classification of urban point clouds. The fields to which that research may be applied are vast, as it provides the ability to increase productivity in regards to the management of urban infrastructures. Moreover, this type of data has the potential to be crucial in the field of autonomous vehicles.
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Communication dans un congrès
CVPR Workshop on Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision, Jun 2018, Salt Lake City, United States
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https://hal.archives-ouvertes.fr/hal-01959556
Contributeur : Jean-Emmanuel Deschaud <>
Soumis le : mardi 18 décembre 2018 - 18:03:27
Dernière modification le : jeudi 7 février 2019 - 15:36:35
Document(s) archivé(s) le : mercredi 20 mars 2019 - 11:51:44

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  • HAL Id : hal-01959556, version 1

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Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette. Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification. CVPR Workshop on Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision, Jun 2018, Salt Lake City, United States. 〈hal-01959556〉

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