Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification
| Autor: | Jean-Emmanuel Deschaud, François Goulette, Xavier Roynard |
|---|---|
| Přispěvatelé: | Centre de Robotique (CAOR), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Contextual image classification business.industry Computer science Feature extraction Point cloud [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 02 engineering and technology Image segmentation computer.software_genre 01 natural sciences [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Field (computer science) Object detection Lidar 11. Sustainability 0202 electrical engineering electronic engineering information engineering Robot [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Data mining Artificial intelligence business computer 0105 earth and related environmental sciences |
| Zdroj: | CVPR Workshop on Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision CVPR Workshop on Real-World Challenges and New Benchmarks for Deep Learning in Robotic Vision, Jun 2018, Salt Lake City, United States CVPR Workshops |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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