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README.md

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-<h1 align="center">EDGS: Eliminating Densification for Efficient Convergence of 3DGS</h2>
-
-<p align="center">
-  <a href="https://www.linkedin.com/in/dmitry-kotovenko-dl/">Dmytro Kotovenko</a><sup>*</sup> ·
-  <a href="https://www.linkedin.com/in/grebenkovao/">Olga Grebenkova</a><sup>*</sup> ·
-  <a href="https://ommer-lab.com/people/ommer/">Björn Ommer</a>
-</p>
-
-<p align="center">CompVis @ LMU Munich · Munich Center for Machine Learning (MCML) </p>
-<p align="center">* equal contribution </p>
-
-<p align="center">
-  <a href="https://compvis.github.io/EDGS/"><img src="https://img.shields.io/badge/Project-Page-blue" alt="Project Page"></a>
-  <a href="https://arxiv.org/pdf/2504.13204"><img src="https://img.shields.io/badge/arXiv-PDF-b31b1b" alt="Paper"></a>
-  <a href="https://colab.research.google.com/github/CompVis/EDGS/blob/main/notebooks/fit_model_to_scene_full.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-  <a href="https://huggingface.co/spaces/CompVis/EDGS"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue" alt="Hugging Face"></a>
-  
-</p>
-
-<p align="center">
-  <img src="./assets/Teaser2.png" width="99%">
-</p>
-
-<p>
-<strong>3DGS</strong> initializes with a sparse set of Gaussians and progressively adds more in under-reconstructed regions. In contrast, <strong>EDGS</strong> starts with
-a dense initialization from triangulated 2D correspondences across training image pairs, 
-requiring only minimal refinement. This leads to <strong>faster convergence</strong> and <strong>higher rendering quality</strong>. Our method reaches the original 3DGS <strong>LPIPS score in just 25% of the training time</strong> and uses only <strong>60% of the splats</strong>. 
-Renderings become <strong>nearly indistinguishable from ground truth after only 3,000 steps — without any densification</strong>.
-</p>
-
-<h3 align="center">3D scene reconstruction using our method in 11 seconds.</h3>
-<p align="center">
-  <img src="assets/video_fruits_our_optimization.gif" width="480" alt="3D Reconstruction Demo">
-</p>
-
-
-
-## 📚 Table of Contents
-- [🚀 Quickstart](#sec-quickstart)
-- [🛠️ Installation](#sec-install)
-- [📦 Data](#sec-data)
-
-- [🏋️ Training](#sec-training)
-- [🏗️ Reusing Our Model](#sec-reuse)
-- [📄 Citation](#sec-citation)
-
-<a id="sec-quickstart"></a>
-## 🚀 Quickstart
-The fastest way to try our model is through the [Hugging Face demo](https://huggingface.co/spaces/magistrkoljan/EDGS), which lets you upload images or a video and interactively rotate the resulting 3D scene. For broad accessibility, we currently support only **forward-facing scenes**.
-#### Steps:
-1. Upload a list of photos or a single video.
-2. Click **📸 Preprocess Input** to estimate 3D positions using COLMAP.
-3. Click **🚀 Start Reconstruction** to run the model.
-
-You can also **explore the reconstructed scene in 3D** directly in the browser.
-
-> ⚡ Runtime: EDGS typically takes just **10–20 seconds**, plus **5–10 seconds** for COLMAP processing. Additional time may be needed to save outputs (model, video, 3D preview).
-
-You can also run the same app locally on your machine with command: 
-```CUDA_VISIBLE_DEVICES=0 python gradio_demo.py --port 7862 --no_share```
-Without `--no_share` flag you will get the adress for gradio app that you can share with the others allowing others to process their data on your server. 
-
-Alternatively, check our [Colab notebook](https://colab.research.google.com/github/CompVis/EDGS/blob/main/notebooks/fit_model_to_scene_full.ipynb).
-
-### 
-
-
-
-<a id="sec-install"></a>
-## 🛠️ Installation
-
-You can either run `install.sh` or manually install using the following:
-
-```bash
-git clone git@github.com:CompVis/EDGS.git --recursive
-cd EDGS
-git submodule update --init --recursive 
-
-conda create -y -n edgs python=3.10 pip
-conda activate edgs
-
-# Set up path to your CUDA. In our experience similar versions like 12.2 also work well 
-export CUDA_HOME=/usr/local/cuda-12.1
-export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
-export PATH=$CUDA_HOME/bin:$PATH
-
-conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia -y
-conda install nvidia/label/cuda-12.1.0::cuda-toolkit -y
-
-pip install -e submodules/gaussian-splatting/submodules/diff-gaussian-rasterization
-pip install -e submodules/gaussian-splatting/submodules/simple-knn
-
-# For COLMAP and pycolmap
-# Optionally install original colmap but probably pycolmap suffices
-# conda install conda-forge/label/colmap_dev::colmap
-pip install pycolmap
-
-
-pip install wandb hydra-core tqdm torchmetrics lpips matplotlib rich plyfile imageio imageio-ffmpeg
-conda install numpy=1.26.4 -y -c conda-forge --override-channels
-
-pip install -e submodules/RoMa
-conda install anaconda::jupyter --yes
-
-# Stuff necessary for gradio and visualizations
-pip install gradio 
-pip install plotly scikit-learn moviepy==2.1.1 ffmpeg
-pip install open3d 
-```
-
-<a id="sec-data"></a>
-## 📦 Data
-
-We evaluated on the following datasets:
-
-- **MipNeRF360** — download [here](https://jonbarron.info/mipnerf360/). Unzip "Dataset Pt. 1" and "Dataset Pt. 2", then merge scenes.
-- **Tanks & Temples + Deep Blending** — from the [original 3DGS repo](https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/datasets/input/tandt_db.zip).
-
-### Using Your Own Dataset
-
-You can use the same data format as the [3DGS project](https://github.com/graphdeco-inria/gaussian-splatting?tab=readme-ov-file#processing-your-own-scenes). Please follow their guide to prepare your scene.
-
-Expected folder structure:
-```
-scene_folder
-|---images
-|   |---<image 0>
-|   |---<image 1>
-|   |---...
-|---sparse
-    |---0
-        |---cameras.bin
-        |---images.bin
-        |---points3D.bin
-```
-
-Nerf synthetic format is also acceptable. 
-
-You can also use functions provided in our code to convert a collection of images or a sinlge video into a desired format. However, this may requre tweaking and processing time can be large for large collection of images with little overlap.
-
-<a id="sec-training"></a>
-## 🏋️ Training
-
-
-To optimize on a single scene in COLMAP format use this code.  
-```bash
-python train.py \
-  train.gs_epochs=30000 \
-  train.no_densify=True \
-  gs.dataset.source_path=<scene folder> \
-  gs.dataset.model_path=<output folder> \
-  init_wC.matches_per_ref=20000 \
-  init_wC.nns_per_ref=3 \
-  init_wC.num_refs=180
-```
-<details>
-<summary><span style="font-weight: bold;">Command Line Arguments for train.py</span></summary>
-  
-  * `train.gs_epochs`
-  Number of training iterations (steps) for Gaussian Splatting.
-  * `train.no_densify`
-  Disables densification. True by default.
-  * `gs.dataset.source_path`
-  Path to your input dataset directory. This should follow the same format as the original 3DGS dataset structure.
-  * `gs.dataset.model_path`
-  Output directory where the trained model, logs, and renderings will be saved.
-  * `init_wC.matches_per_ref`
-  Number of 2D feature correspondences to extract per reference view for initialization. More matches leads to more gaussians.
-  * `init_wC.nns_per_ref`
-  Number of nearest neighbor images used per reference during matching.
-  * `init_wC.num_refs`
-  Total number of reference views sampled. 
-  * `wandb.mode`
-    Specifies how Weights & Biases (W&B) logging is handled.
-
-    - Default: `"disabled"`
-    - Options:
-      - `"online"` — log to the W&B server in real-time
-      - `"offline"` — save logs locally to sync later
-      - `"disabled"` — turn off W&B logging entirely
-
-    If you want to enable W&B logging, make sure to also configure:
-
-    - `wandb.project` — the name of your W&B project
-    - `wandb.entity` — your W&B username or team name
-
-Example override:
-```bash
-wandb.mode=online wandb.project=EDGS wandb.entity=your_username train.gs_epochs=15_000 init_wC.matches_per_ref=15_000
-```
-</details>
-<br>
-
-To run full evaluation on all datasets:
-
-```bash
-python full_eval.py -m360 <mipnerf360 folder> -tat <tanks and temples folder> -db <deep blending folder>
-```
-<a id="sec-reuse"></a>
-## 🏗️ Reusing Our Model
-
-Our model is essentially a better **initialization module** for Gaussian Splatting. You can integrate it into your pipeline by calling:
-
-```python
-source.corr_init.init_gaussians_with_corr(...)
-```
-### Input arguments:
-- A GaussianModel and Scene instance
-- A configuration namespace `cfg.init_wC` to specify parameters like the number of matches, neighbors, and reference views
-- A RoMA model (automatically instantiated if not provided)
-
-
-
-<a id="sec-citation"></a>
-## 📄 Citation
-```bibtex
-@misc{kotovenko2025edgseliminatingdensificationefficient,
-      title={EDGS: Eliminating Densification for Efficient Convergence of 3DGS}, 
-      author={Dmytro Kotovenko and Olga Grebenkova and Björn Ommer},
-      year={2025},
-      eprint={2504.13204},
-      archivePrefix={arXiv},
-      primaryClass={cs.GR},
-      url={https://arxiv.org/abs/2504.13204}, 
-}
-```
 ---
-
-# TODO:
-- [ ] Code for training and processing forward-facing scenes.
-- [ ] More data examples
-
-
-
+title: Final de Moviles-3dgs
+emoji: 🚀
+colorFrom: blue
+colorTo: purple
+sdk: docker
+app_file: main.py
+pinned: false
+---