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| import torch | |
| CSS = """ | |
| #img-display-container { | |
| max-height: 160vh; | |
| } | |
| #img-display-input { | |
| max-height: 140vh; | |
| } | |
| #img-display-output { | |
| max-height: 70vh; | |
| } | |
| #pcd-display-output { | |
| max-height: 70vh; | |
| } | |
| """ | |
| HEADER = ''' | |
| # LiDAR Diffusion | |
| Official demo for **LiDAR Diffusion: Towards Realistic Scene Generation with LiDAR Diffusion Models**. | |
| Please refer to our [paper](https://arxiv.org/abs/2404.00815), [project page](https://lidar-diffusion.github.io/), or [github](https://github.com/hancyran/LiDAR-Diffusion) for more details. | |
| ### Camera-to-LiDAR Demo | |
| ''' | |
| FOOTER = r""" | |
| ```bibtex | |
| @inproceedings{ran2024towards, | |
| title={Towards Realistic Scene Generation with LiDAR Diffusion Models}, | |
| author={Ran, Haoxi and Guizilini, Vitor and Wang, Yue}, | |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | |
| year={2024} | |
| } | |
| ``` | |
| """ | |