4DGaussians

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4DGaussians is a Research & Education tool that enables real-time dynamic scene rendering using 4D Gaussian Splatting. It offers fast convergence and real-time rendering speeds for dynamic scenes.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is 4DGaussians?

4DGaussians is a research project presented at CVPR 2024, focusing on 4D Gaussian Splatting for real-time dynamic scene rendering. This method allows for very quick convergence and achieves real-time rendering speeds, as demonstrated on D-NeRF and HyperNeRF datasets. The project provides code for environmental setup, data preparation for synthetic and real dynamic scenes (D-NeRF, HyperNeRF, DyNeRF, and multiple views), training, rendering, and evaluation. It also includes helpful scripts for exporting 3D Gaussians, visualizing weights, and merging 4D Gaussians, making it a comprehensive resource for researchers in computer vision and graphics.

Best used for

Ideal for professors and researchers who need to explore and implement real-time dynamic scene rendering using 4D Gaussian Splatting. Especially valuable for academic research in computer vision and graphics, enabling rapid experimentation and evaluation of novel view synthesis techniques.

Common actions

render dynamic scenes

research 4D graphics

evaluate rendering models

process 3D data

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Capabilities

Key features

Real-time dynamic scene rendering
4D Gaussian Splatting
Fast convergence
Data preparation scripts
Training and rendering scripts
Model evaluation
3D Gaussian export

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of datasets does 4DGaussians support for training?

4DGaussians supports various datasets including synthetic scenes from D-NeRF, real dynamic scenes from HyperNeRF and DyNeRF, and custom multiple-view scenes. It provides scripts to help organize and preprocess these datasets for training.

How fast is the training and rendering process with 4DGaussians?

The method boasts very fast convergence and real-time rendering speeds. For instance, training on D-NeRF datasets can take as little as 8 minutes, and HyperNeRF datasets around 30 minutes, with rendering happening in real-time.

Can I use 4DGaussians for my own custom datasets?

Yes, 4DGaussians can be adapted for custom datasets. Users are guided to install nerfstudio and use its COLMAP pipeline to process their image data and generate the necessary camera poses and point clouds for training.

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Content & Design › 3D & Animation

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