SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Open accessAbstract
Accurate scene understanding from multiple sensors mounted on cars is a key requirement for autonomous driving systems. Nowadays, this task is mainly performed through data-hungry deep learning techniques that need very large amounts of data to be trained. Due to the high cost of performing segmentation labeling, many synthetic datasets have been proposed. However, most of them miss the multi-sensor nature of the data, and do not capture the significant changes introduced by the variation of daytime and weather conditions. To fill these gaps, we introduce SELMA, a novel synthetic dataset for semantic segmentation that contains more than 30K unique waypoints acquired from 24 different sensors including RGB, depth, semantic cameras and LiDARs, in 27 different weather and daytime conditions, for a total of more than 20M samples. SELMA is based on CARLA, an open-source simulator for generating synthetic data in autonomous driving scenarios, that we modified to increase the variability and the diversity in the scenes and class sets, and to align it with other benchmark datasets. As shown by the experimental evaluation, SELMA allows the efficient training of standard and multi-modal deep learning architectures, and achieves remarkable results on real-world data. SELMA is free and publicly available, thus supporting open science and research.
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LiDAR is a long-range, omnidirectional depth sensor with high robustness in geometry acquisition at higher cost.
“A LiDAR is a long-range, omnidirectional depth sensor, which comes with high robustness in geometry acquisition at the expense of a higher cost [39].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [39]
SELMA is a synthetic dataset developed in a modified CARLA simulator whose goal is to provide multimodal data in a multitude of environmental conditions, while also allowing a researcher to control such conditions.
“is a very recent (2022) synthetic dataset developed in a modified CARLA simulator [96] whose goal is to provide multimodal data in a multitude of environmental conditions, while also allowing a researcher to control such conditions.”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [39]
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- Authors
- Testolina, P., Barbato, F., Michieli, U., Giordani, M., Zanuttigh, P., & Zorzi, M.
- Affiliations
- University of Padua (IT)
- ORCID
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2023-07
- Volume
- 24
- Issue
- 7
- Pages
- 7012-7024
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W4361029874
- Cited by
- 41
- References
- 80
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 15 · 2023: 12 · 2022: 1
- FWCI
- 3.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- European Union through the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future” (PE0000001)
- Fondazione Cassa di Risparmio di Padova e Rovigo (“Dottorati di Ricerca” 2019)
- University of Padova under the SID project “Semantic Segmentation in the Wild.”
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Selma, synthetic dataset, semantic segmentation, autonomous driving, multi-sensor data, LiDAR, CARLA simulator, scene understanding, multimodal data
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