Updated Readme

README.md

@@ -1,54 +1,12 @@
-# Splatt3R: Zero-shot Gaussian Splatting from Uncalibarated Image Pairs
-
-Official implementation of `Zero-shot Gaussian Splatting from Uncalibarated Image Pairs`
-
-Links removed for anonymity: 
-[Project Page](), [Splatt3R arXiv]()
-
-## Installation
-
-1. Clone Splatt3R 
-```bash
-git clone <redacted github link>
-cd splatt3r
-```
-
-2. Setup Anaconda Environment
-```bash
-conda env create -f environment.yml
-pip install git+https://github.com/dcharatan/diff-gaussian-rasterization-modified
-```
-
-3. (Optional) Compile the CUDA kernels for RoPE (as in MASt3R and CroCo v2)
-
-```bash
-cd src/dust3r_src/croco/models/curope/
-python setup.py build_ext --inplace
-cd ../../../../../
-```
-
-## Checkpoints
-
-We train our model using the pretrained `MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric` checkpoint from the MASt3R authors, available from [the MASt3R GitHub repo](https://github.com/naver/mast3r). This checkpoint is placed at the file path `checkpoints/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth`.
-
-A pretrained Splatt3R model can be downloaded [here]() (redacted link).
-
-## Data
-
-We use ScanNet++ to train our model. We download the data from the [official ScanNet++ homepage](https://kaldir.vc.in.tum.de/scannetpp/) and process the data using SplaTAM's modified version of [the ScanNet++ toolkit](https://github.com/Nik-V9/scannetpp). We save the processed data to the 'processed' subfolder of the ScanNet++ root directory.
-
-Our generated test coverage files, and our training and testing splits, can be downloaded [here]() (redacted link), and placed in `data/scannetpp`.
-
-## Demo
-
-The Gradio demo can be run using `python demo.py <checkpoint_path>`, replacing `<checkpoint_path>` with the trained network path. A checkpoint will be available for the public release of this code.
-
-This demo generates a `.ply` file that represents the scene, which can be downloaded and rendered using online 3D Gaussian Splatting viewers such as [here](https://projects.markkellogg.org/threejs/demo_gaussian_splats_3d.php?art=1&cu=0,-1,0&cp=0,1,0&cla=1,0,0&aa=false&2d=false&sh=0) or [here](https://playcanvas.com/supersplat/editor).
-
-## Training
-
-Our training run can be recreated by running `python main.py configs/main.yaml`. Other configurations can be found in the `configs` folder.
-
-## BibTeX
-
-Forthcoming arXiv citation
+---
+title: Splatt3R: Zero-shot Gaussian Splatting from Uncalibarated Image Pairs
+emoji: ⛰️
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 4.37.2
+app_file: demo.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference