Awesome

BEINGS: Bayesian Embodied Image-goal Navigation with Gaussian Splatting

This repository includes the code that can be used to reproduce the results of our paper BEINGS: Bayesian Embodied Image-goal Navigation with Gaussian Splatting

Clone the repo

git clone https://github.com/guaMass/BEINGS.git --recursive

Download scences

Download scences from our website, and put them in scence folder. Or just unzip the zip file in the project's root path.

Enviroment configuration

Create a new conda enviroment

conda create -n beings python=3.10 
conda activate beings

Install CUDA and Visual studio, add them to PATH. Install Pytorch

conda install -c "nvidia/label/cuda-11.6.0" cuda-toolkit
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge

Install requirments

Run

Edit ./src/beings.py before you running.

• model_path: Use the .ply path of scence folder.
• task_name: Use the target image that you what the robot find.

Run ./src/beings.py.

Visualization

There are two ways to view the results of BEINGS. If you choose to log while BEINGS is running, you can watch the results at each time step in real time via the notebook. Or at the end of a BEINGS run, view the animation of the run via ./src/visual_animation_3d.py.

Run with your own map

Feel free to try BEINGS on your own 3DGS file (must be .ply formmat) and your own images.

Demonstration

Acknowledgement

• SuperSplat - Open source browser-based tool to clean/filter, reorient and compress .ply/.splat files.
• nerfstudio - Open source tool to generate 3DGS file from images.
• LUMA AI - Commercial tool to generate 3DGS file with smart phone.
• SpectacularAI - Conversion scripts for different 3DGS conventions.
• awesome-3D-gaussian-splatting - Curated list of papers and resources focused on 3D Gaussian Splatting, intended to keep pace with the anticipated surge of research in the coming months.
• PythonRobotics - Some of the utility functions used in the coordinate conversion processes have been sourced from this open-source library.
• PatchNetVLAD - CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"