Multi-modal panoramic 3D outdoor datasets for place categorization
Open accessAbstract
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).
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The MPO dataset contains 650 dense scans and 34200 sparse scans.
“650 scans (dense), 34200 scans (sparse)”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [77]
The MPO dataset is for place categorization.
“Place categorization”
From Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2021) · cites this paper as [77]
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SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498 · cited 2× · see citing works
“In this work we compared three feature descriptors that can be applied to 3D and multimodal sensor cues: local binary patterns (LBP) [17], spin images [18], and textons [19].”
MethodLarge-scale semantic mapping and reasoning with heterogeneous modalities
Pronobis, A., & Jensfelt, P. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224637 · Cited by 248 · see citing works
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nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
“Other notable multimodal datasets include [15] providing driving behavior labels, [43] providing place categorization labels and [6, 55] providing raw data without semantic labels.”
BackgroundFeng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
- Authors
- Jung, H., Oto, Y., Mozos, O. M., Iwashita, Y., & Kurazume, R.
- Affiliations
- Kyushu University (JP)
- Jet Propulsion Laboratory (US)
- Venue
- 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 4545-4550
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2566683698
- Cited by
- 16
- References
- 3
- Citations by year
- 2025: 1 · 2024: 2 · 2022: 2 · 2021: 4 · 2020: 4 · 2019: 1 · 2018: 1 · 2017: 1
- FWCI
- 3.66
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- outdoor environments, point clouds, multimodal dataset, mobile laser scanning, forests, coast, residential areas, urban environment, parking lots
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