BestWJH/VRU_Basketball

This is a basketball dataset from AVS-VRU, which contains two dynamic basketball court scenes collected in Guangzhou(gz) and Dongguan(dg). It features large-scale motion, making it well-suited for evaluating the performance of dynamic methods. Each scene contains 36 viewpoints, with videos captured at 1080p resolution for each viewpoint. The duration of each video is approximately 10 seconds, consisting of 250 frames. Viewpoints 0, 10, 20, and 30 are used as test views, while the remaining viewpoints are used for training.

It was presented in the paper Swift4D and LocalDyGS.

If you find it useful, we would appreciate it if you could cite our paper:

@article{wu2025swift4d,
  title={Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene},
  author={Wu, Jiahao and Peng, Rui and Wang, Zhiyan and Xiao, Lu and Tang, Luyang and Yan, Jinbo and Xiong, Kaiqiang and Wang, Ronggang},
  journal={arXiv preprint arXiv:2503.12307},
  year={2025}
}

@article{wu2025localdygs,
  title={LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling},
  author={Wu, Jiahao and Peng, Rui and Jiao, Jianbo and Yang, Jiayu and Tang, Luyang and Xiong, Kaiqiang and Liang, Jie and Yan, Jinbo and Liu, Runling and Wang, Ronggang},
  journal={arXiv preprint arXiv:2507.02363},
  year={2025}
}