VastGaussian

Unofficial implementation of Vast 3D Gaussians for Large Scene Reconstruction

CommonProductImage3D scene reconstructioncomputer vision

VastGaussian is an open-source project for 3D scene reconstruction that leverages 3D Gaussians to model the geometric and appearance information of large-scale scenes. This project is an implementation from scratch and may contain some errors, but it offers a new perspective for the field of 3D scene reconstruction. Key advantages include its ability to process large datasets and the improvements made to the original 3DGS project, enhancing its clarity and usability.

Camera-position-based region division
Position-based data selection
Visibility-based camera selection
Coverage-based point selection
Decoupled Appearance Modeling
Seamless Merging
Parallel training of m√ón regions on a single GPU

VastGaussian is designed for researchers and developers in the fields of 3D scene reconstruction
computer vision
and graphics. It is particularly suited for them because it offers a novel approach to 3D scene reconstruction that can handle large datasets and improves upon the original 3DGS project for better understandability and usability.

Application on the UrbanScene3D dataset
Application on the Mill-19 dataset
Application on the tantid_db dataset

Clone or download the VastGaussian project locally
Set up the environment according to the project documentation
including installing necessary dependency libraries
Adjust parameters in the arguments/parameters.py file to suit your dataset and needs
Start training the VastGaussian model using the train_vast.py file

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