All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908336888217600 |
|---|---|
| author | Gómez, Jose L. Silva, Manuel Seoane, Antonio Borrás, Agnès Noriega, Mario Ros, Germán Iglesias-Guitian, Jose A. López, Antonio M. |
| author_facet | Gómez, Jose L. Silva, Manuel Seoane, Antonio Borrás, Agnès Noriega, Mario Ros, Germán Iglesias-Guitian, Jose A. López, Antonio M. |
| contents | We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (www.urbansyn.org). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12176 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes Gómez, Jose L. Silva, Manuel Seoane, Antonio Borrás, Agnès Noriega, Mario Ros, Germán Iglesias-Guitian, Jose A. López, Antonio M. Computer Vision and Pattern Recognition We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (www.urbansyn.org). |
| title | All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.12176 |