Home Projects taichi_3d_gaussian_splatting
taichi_3d_gaussian_splatting
Jupyter Notebook

taichi_3d_gaussian_splatting

An unofficial implementation of paper 3D Gaussian Splatting for Real-Time Radiance Field Rendering by taichi lang.

by wanmeihuali · GitHub
Stars
Forks
License
Created
Last commit
3d-reconstruction3d-renderingcomputer-graphicsApache-2.0Jupyter Notebook
View on GitHub
In plain words

Render 3D images in real-time by processing multiple views and point clouds with a specific algorithm.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
taichi_3d_gaussian_splatting — GitHub preview card
📈 Star history
752751
2026-07-202026-08-31
📈 Track taichi_3d_gaussian_splatting

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

An unofficial implementation of paper 3D Gaussian Splatting for Real-Time Radiance Field Rendering by taichi lang.

taichi_3d_gaussian_splatting has 751 stars on GitHub. It has been forked 72 times. taichi_3d_gaussian_splatting is written mainly in Jupyter Notebook. It has been in active development since 2023. taichi_3d_gaussian_splatting is available under the Apache-2.0 license. Its main topics are 3d-reconstruction, 3d-rendering, computer-graphics, computer-vision.

Frequently asked questions

What is taichi_3d_gaussian_splatting?

An unofficial implementation of paper 3D Gaussian Splatting for Real-Time Radiance Field Rendering by taichi lang.

Is taichi_3d_gaussian_splatting open source?

taichi_3d_gaussian_splatting is an open-source project. It is released under the Apache-2.0 license.

Is taichi_3d_gaussian_splatting free?

Yes. taichi_3d_gaussian_splatting is free and open source — you can use, modify and self-host it.

What license does taichi_3d_gaussian_splatting use?

taichi_3d_gaussian_splatting is available under the Apache-2.0 license.

What language is taichi_3d_gaussian_splatting written in?

taichi_3d_gaussian_splatting is written mainly in Jupyter Notebook.

🏅 Maintainer of this project?
olud.ai badge — taichi_3d_gaussian_splatting

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=wanmeihuali-taichi-3d-gaussian-splatting)](https://olud.ai/project/wanmeihuali-taichi-3d-gaussian-splatting.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →
ScanNet
2.3k · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
neural-rgbd-surface-reconstruction
Official implementation of the CVPR 2022 Paper "Neural RGB-D Surface Reconstruction"
631 · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
humanrf
Official code for "HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion"
496 · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
HashNeRF-pytorch
Pure PyTorch Implementation of NVIDIA paper on Instant Training of Neural Graphics primitives:…
1k · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
Instant-angelo
Instant-angelo: Build high-fidelity Digital Twin within 20 Minutes!
462 · 3d
sharescomputer-graphics3d-reconstruction
instant-ngp
Instant neural graphics primitives: lightning fast NeRF and more
17.5k · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
Structured3D
[ECCV'20] Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
674 · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
Skyfall-GS
[ECCV 2026] Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery
927 · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
ECON
[CVPR'23, Highlight] ECON: Explicit Clothed humans Optimized via Normal integration
1.2k · 3d-reconstruction
sharescomputer-graphics3d-reconstruction
neuralangelo
Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 20…
4.6k · 3d-reconstruction
sharescomputer-graphics3d-reconstruction

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.