Cleaned up README and added visualization tutorials for Prerelease!

README.md

@@ -1,5 +1,10 @@
-# UT CODa Model Training Configurations
-We utilize the ST3D++ code repository and OpenPCDet model training configuration files for all empirical analyses conducted for the UT CODa paper.
+# University of Texas Campus Object Dataset Object Detection Models
+
+<b>Official model development kit for CODa.</b> We strongly recommend using this repository to run our pretrained
+models and train on custom datasets. Thanks to the authors of ST3D++ and OpenPCDet from whom this repository
+was adapted from.
+
+![Sequence 0 Clip](./docs/codademo.gif)
 
 ## Installation
 
@@ -13,13 +18,9 @@ Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) to learn more usag
 
 Our code is released under the Apache 2.0 license.
 
-## Acknowledgement
-
-Our code is heavily based on [OpenPCDet v0.3](https://github.com/open-mmlab/OpenPCDet/commit/e3bec15f1052b4827d942398f20f2db1cb681c01). Thanks OpenPCDet Development Team for their awesome codebase.
-
-## Citation
+## Paper Citation
 
-If you find our work useful in your research, please consider citing:
+If you find our work useful in your research, please consider citing our work:
 ```
 @inproceedings{zhang2023utcoda,
     title={Towards Robust 3D Robot Perception in Urban Environments: The UT Campus Object Dataset},
@@ -29,15 +30,25 @@ If you find our work useful in your research, please consider citing:
 }
 ```
 
-If you find the ST3D++ or OpenPCDet useful, please cite:
+## Dataset Citation
 ```
-@inproceedings{yang2021st3d,
-    title={ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection},
-    author={Yang, Jihan and Shi, Shaoshuai and Wang, Zhe and Li, Hongsheng and Qi, Xiaojuan},
-    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-    year={2021}
+@data{T8/BBOQMV_2023,
+author = {Zhang, Arthur and Eranki, Chaitanya and Zhang, Christina and Hong, Raymond and Kalyani, Pranav and Kalyanaraman, Lochana and Gamare, Arsh and Bagad, Arnav and Esteva, Maria and Biswas, Joydeep},
+publisher = {Texas Data Repository},
+title = {{UT Campus Object Dataset (CODa)}},
+year = {2023},
+version = {DRAFT VERSION},
+doi = {10.18738/T8/BBOQMV},
+url = {https://doi.org/10.18738/T8/BBOQMV}
 }
 ```
+
+## Acknowledgement
+
+Our code is heavily based on [OpenPCDet v0.3](https://github.com/open-mmlab/OpenPCDet/commit/e3bec15f1052b4827d942398f20f2db1cb681c01). Thanks OpenPCDet Development Team for their awesome codebase.
+
+
+Thank you to the authors of ST3D++ or OpenPCDet for an awesome codebase!
 ```
 @article{yang2021st3d++,
   title={ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection},