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README.md

@@ -31,20 +31,29 @@ pip3 install -e git+https://github.com/open-mmlab/mmgeneration@f6551e1d6ca24121d
 ```
 
 ## Static Reconstruction
+The project structure follows the origianl 3DGS repository. 
 
 ### Blender Dataset
+To run SplatFields on the Blender dataset, download the origianl [NeRF synthetic dataset](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) and follow the instructions in the `run_blender.sh` script. 
+
+Make sure that the `DATASET_ROOT` variable is set to the directory where the Blender dataset is downloaded. 
+
 ### DTU Dataset
+To run our method on the DTU dataset, you could directly download the pre-processed subset released in the [2DGS repo](https://drive.google.com/drive/folders/1SJFgt8qhQomHX55Q4xSvYE2C6-8tFll9). 
+We provide the bash script `run_dtu.sh` to run SplatFields on the DTU sequences. Update the `DATASET_ROOT` in the script to the path of the downloaded dataset. 
 
 ## Dynamic Reconstruction
+SplatFields straightforwardly extends to dynamic scenes. 
 
 ### Owlii Dataset
+The pre-processed Owlii dataset for the dynamic sparse view reconstruction is available [here](https://drive.google.com/file/d/1OdqwXKmvnpxFI4LC8ckI0eV6r9EZ64ZV/view?usp=sharing) (or download it via `gdown 1OdqwXKmvnpxFI4LC8ckI0eV6r9EZ64ZV && unzip DATA_OWLII`). Then, run the script `run_owlii.sh` to train our model on varying configurations (set the data directory appropriately). 
 
 ## Citation
 
 If you find our work helpful, please consider citing:
 ```bibtex
 @inproceedings{SplatFields,
-   title={{SplatFields}: SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction},
+   title={SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction},
    author={Mihajlovic, Marko and Prokudin, Sergey and Tang, Siyu and Maier, Robert and Bogo, Federica and Tung, Tony and Boyer, Edmond},
    booktitle={European Conference on Computer Vision (ECCV)},
    year={2024},

run_owlii.sh

 # Example script to run SplatFields4D on the Owlii dataset
-DATASET_ROOT=../ResFields/datasets/public_data
-SCENE=dancer_vox11 # exercise_vox11 model basketball
+# Download the dataset via `gdown 1OdqwXKmvnpxFI4LC8ckI0eV6r9EZ64ZV && unzip DATA_OWLII`
+DATASET_ROOT=./DATA_OWLII
+SCENE=dancer # exercise model basketball
 N_VIEWS=8 # 10 8 6 4
 
 python train.py -s $DATASET_ROOT/$SCENE --white_background --eval --load_time_step 100  -m ./output_rep/Owlii/${N_VIEWS}views/$SCENE/SplatFields4D --flow_model offset --all_training --train_cam_names cam_train_0 cam_train_1 cam_train_2 cam_train_3 cam_train_4 cam_train_5 cam_train_6 cam_train_7 cam_train_8 cam_train_9 --pts_samples hull --iterations 200000 --encoder_type VarTriPlaneEncoder --num_pts 100000 --num_views 5 --composition_rank 40