upload code

docs/README.md

@@ -0,0 +1,58 @@
+# [ECCV 24] Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering
+
+Welcome to the **official code repository** for our paper _"Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering"_, accepted at **ECCV 2024**. 🚀
+
+Our framework offers **state-of-the-art performance** by combining multiple experts in a model-agnostic manner to enhance the quality and efficiency of NeRF-based rendering.
+
+![Teaser Image 1](method.jpg)  
+<!-- ![Teaser Image 2](path_to_image2) -->
+
+---
+
+## 🔗 Project Page
+Explore the full project details and supplementary materials on our [**Project Page**](https://eidoslab.github.io/boost-your-nerf/).
+
+---
+
+
+## 📊 Datasets
+- Tested datasets:
+    - *Bounded inward-facing*: [NeRF](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1), [NSVF](https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip), [T&T (masked)](https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip).
+    - *Foward-facing*: [LLFF](https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7).
+
+---
+## 🚀 Quickstart
+
+We offer our implementation with 3 main Fast-NeRFs architectures: **DVGO**, **TensoRF** and **Instant-NGP**. So far the code is available for DVGO: we will update our repo soon with the other method. 
+
+Our method works under the same configuration of the original implementation of [**DVGO**](https://github.com/sunset1995/DirectVoxGO). So, the first step is to clone from thre and install all the dependencies:
+
+
+```bash
+git clone [email protected]:sunset1995/DirectVoxGO.git
+cd DirectVoxGO
+pip install -r requirements.txt
+```
+
+Then, clone our repository and **copy** the following files into the root directory of DVGO:
+
+- `train_single_model.py`: A wrapper to the original DVGO method to train multiple models at different resolutions.
+- `run_dvgo.py`: The main DVGO file with minor changes.
+- `moe.py`: Contains all the logic behind our Sparse-Mixture of Experts framework.
+- `moe_main.py`: The main file of our method.
+
+The first step is to pre-train models at different resolutions:
+
+
+```bash
+python3 train_single_model.py --dataset_name nerf --scene lego --resolutions 128 160 200 256 300 350 --datadir path_to_datadir --render_test --eval_ssim --eval_lpips_alex
+```
+
+We are now ready to ready to train our MoE:
+
+
+```bash
+python3 moe_main.py --dataset_name nerf --scene lego --resolutions 128 160 200 256 300 350 --datadir path_to_datadir --render_test --eval_ssim --eval_lpips_alex --top_k 2 --num_experts 5
+```
+
+This code trains our MoE with *five experts* (the first model with a res of 128^3 is excluded as it is used for initializing the gate).