init

app.py

@@ -6,7 +6,7 @@ import torch
 import numpy as np
 from matplotlib.colors import LinearSegmentedColormap
 
-from app_config import CSS, TITLE, DESCRIPTION, DEVICE
+from app_config import CSS, TITLE, DESCRIPTION, BIBTEX, DEVICE
 import sample_cond
 
 model = sample_cond.load_model()
@@ -76,6 +76,8 @@ with gr.Blocks(css=CSS) as demo:
     examples = gr.Examples(examples=example_files, inputs=[input_image], outputs=[output_image, raw_file],
                            fn=on_submit, cache_examples=True)
 
+    gr.Markdown(BIBTEX)
+
 
 if __name__ == '__main__':
     demo.queue().launch()

app_config.py

@@ -15,3 +15,13 @@ DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
 TITLE = "# LiDAR Diffusion"
 DESCRIPTION = """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."""
+BIBTEX = 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}
+}
+```
+"""

requirements.txt

@@ -12,5 +12,6 @@ imageio==2.9.0
 imageio-ffmpeg==0.4.2
 opencv-python
 more_itertools
+kornia==0.7.0
 -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
 -e git+https://github.com/openai/CLIP.git@main#egg=clip