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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.16784 |
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| _version_ | 1866915684854792192 |
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| author | Teglia, Simone Tonti, Claudia Melis Pro, Francesco Russo, Leonardo Alfarano, Andrea Pentassuglia, Leonardo Amerini, Irene |
| author_facet | Teglia, Simone Tonti, Claudia Melis Pro, Francesco Russo, Leonardo Alfarano, Andrea Pentassuglia, Leonardo Amerini, Irene |
| contents | Datasets are essential to train and evaluate computer vision models used for traffic analysis and to enhance road safety. Existing real datasets fit real-world scenarios, capturing authentic road object behaviors, however, they typically lack precise ground-truth annotations. In contrast, synthetic datasets play a crucial role, allowing for the annotation of a large number of frames without additional costs or extra time. However, a general drawback of synthetic datasets is the lack of realistic vehicle motion, since trajectories are generated using AI models or rule-based systems. In this work, we introduce R3ST (Realistic 3D Synthetic Trajectories), a synthetic dataset that overcomes this limitation by generating a synthetic 3D environment and integrating real-world trajectories derived from SinD, a bird's-eye-view dataset recorded from drone footage. The proposed dataset closes the gap between synthetic data and realistic trajectories, advancing the research in trajectory forecasting of road vehicles, offering both accurate multimodal ground-truth annotations and authentic human-driven vehicle trajectories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | R3ST: A Synthetic 3D Dataset With Realistic Trajectories Teglia, Simone Tonti, Claudia Melis Pro, Francesco Russo, Leonardo Alfarano, Andrea Pentassuglia, Leonardo Amerini, Irene Computer Vision and Pattern Recognition Datasets are essential to train and evaluate computer vision models used for traffic analysis and to enhance road safety. Existing real datasets fit real-world scenarios, capturing authentic road object behaviors, however, they typically lack precise ground-truth annotations. In contrast, synthetic datasets play a crucial role, allowing for the annotation of a large number of frames without additional costs or extra time. However, a general drawback of synthetic datasets is the lack of realistic vehicle motion, since trajectories are generated using AI models or rule-based systems. In this work, we introduce R3ST (Realistic 3D Synthetic Trajectories), a synthetic dataset that overcomes this limitation by generating a synthetic 3D environment and integrating real-world trajectories derived from SinD, a bird's-eye-view dataset recorded from drone footage. The proposed dataset closes the gap between synthetic data and realistic trajectories, advancing the research in trajectory forecasting of road vehicles, offering both accurate multimodal ground-truth annotations and authentic human-driven vehicle trajectories. |
| title | R3ST: A Synthetic 3D Dataset With Realistic Trajectories |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16784 |