Multi-modal panoramic 3D outdoor datasets for place categorization
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918446956019712 |
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| author | Jung, Hojung Oto, Yuki Mozos, Oscar M. Iwashita, Yumi Kurazume, Ryo |
| author_facet | Jung, Hojung Oto, Yuki Mozos, Oscar M. Iwashita, Yumi Kurazume, Ryo |
| contents | We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13142 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-modal panoramic 3D outdoor datasets for place categorization Jung, Hojung Oto, Yuki Mozos, Oscar M. Iwashita, Yumi Kurazume, Ryo Robotics Computer Vision and Pattern Recognition Databases We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area, urban area and indoor/outdoor parking lot. The first dataset consists of 650 static panoramic scans of dense (9,000,000 points) 3D color and reflectance point clouds obtained using a FARO laser scanner with synchronized color images. The second dataset consists of 34,200 real-time panoramic scans of sparse (70,000 points) 3D reflectance point clouds obtained using a Velodyne laser scanner while driving a car. The datasets were obtained in the city of Fukuoka, Japan and are publicly available in [1], [2]. In addition, we compare several approaches for semantic place categorization with best results of 96.42% (dense) and 89.67% (sparse). |
| title | Multi-modal panoramic 3D outdoor datasets for place categorization |
| topic | Robotics Computer Vision and Pattern Recognition Databases |
| url | https://arxiv.org/abs/2604.13142 |