RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909653886042112 |
|---|---|
| author | Jadon, Arpit Wang, Haoran Thomas, Phillip Stanley, Michael Cibik, S. Nathaniel Laurat, Rachel Maher, Omar Hoyer, Lukas Unal, Ozan Dai, Dengxin |
| author_facet | Jadon, Arpit Wang, Haoran Thomas, Phillip Stanley, Michael Cibik, S. Nathaniel Laurat, Rachel Maher, Omar Hoyer, Lukas Unal, Ozan Dai, Dengxin |
| contents | As perception models continue to develop, the need for large-scale datasets increases. However, data annotation remains far too expensive to effectively scale and meet the demand. Synthetic datasets provide a solution to boost model performance with substantially reduced costs. However, current synthetic datasets remain limited in their scope, realism, and are designed for specific tasks and applications. In this work, we present RealDriveSim, a realistic multi-modal synthetic dataset for autonomous driving that not only supports popular 2D computer vision applications but also their LiDAR counterparts, providing fine-grained annotations for up to 64 classes. We extensively evaluate our dataset for a wide range of applications and domains, demonstrating state-of-the-art results compared to existing synthetic benchmarks. The dataset is publicly available at https://realdrivesim.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving Jadon, Arpit Wang, Haoran Thomas, Phillip Stanley, Michael Cibik, S. Nathaniel Laurat, Rachel Maher, Omar Hoyer, Lukas Unal, Ozan Dai, Dengxin Computer Vision and Pattern Recognition As perception models continue to develop, the need for large-scale datasets increases. However, data annotation remains far too expensive to effectively scale and meet the demand. Synthetic datasets provide a solution to boost model performance with substantially reduced costs. However, current synthetic datasets remain limited in their scope, realism, and are designed for specific tasks and applications. In this work, we present RealDriveSim, a realistic multi-modal synthetic dataset for autonomous driving that not only supports popular 2D computer vision applications but also their LiDAR counterparts, providing fine-grained annotations for up to 64 classes. We extensively evaluate our dataset for a wide range of applications and domains, demonstrating state-of-the-art results compared to existing synthetic benchmarks. The dataset is publicly available at https://realdrivesim.github.io/. |
| title | RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.16319 |