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InstantSplat: Sparse-view Gaussian Splatting in Seconds

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This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting. InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.

Table of Contents

Free-view Rendering

example.mp4

TODO List

  • Support 2D-GS
  • Long sequence cross window alignment
  • Support Mip-Splatting

Get Started

Installation

  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace

Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel

docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.

Usage

  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh

Acknowledgement

This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!

Citation

If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.

@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

About

InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds

instantsplat.github.io/

Resources

Readme
License
Activity
Custom properties

Stars

1.7k stars

Watchers

20 watching

Forks

156 forks
Report repository

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Used by

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
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1.7k stars
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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InstantSplat: Sparse-view  Gaussian Splatting in Seconds 
arXivGradioHome PageXyoutubeyoutube

This repository is the official implementation of InstantSplat, a sparse-view framework for large-scale scene reconstruction method using Gaussian Splatting.
InstantSplat supports 3D-GS, 2D-GS, and Mip-Splatting.


Table of Contents
Free-view Rendering
example.mp4
TODO List
  •  Support 2D-GS
  •  Long sequence cross window alignment
  •  Support Mip-Splatting
Get Started
Installation
  1. Clone InstantSplat and download pre-trained model.
git clone --recursive https://github.com/NVlabs/InstantSplat.git
cd InstantSplat
mkdir -p mast3r/checkpoints/
wget https://download.europe.naverlabs.com/ComputerVision/MASt3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth -P mast3r/checkpoints/
  1. Create the environment (or use pre-built docker), here we show an example using conda.
conda create -n instantsplat python=3.10.13 cmake=3.14.0 -y
conda activate instantsplat
conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia # use the correct version of cuda for your system
pip install -r requirements.txt
pip install submodules/simple-knn
pip install submodules/diff-gaussian-rasterization
pip install submodules/fused-ssim
  1. Optional but highly suggested, compile the cuda kernels for RoPE (as in CroCo v2).
# DUST3R relies on RoPE positional embeddings for which you can compile some cuda kernels for faster runtime.cd croco/models/curope/
python setup.py build_ext --inplace
Alternative: use the pre-built docker image: pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker pull dockerzhiwen/instantsplat_public:2.0

if docker failed to produce reasonable results, try Installation step again within the docker.
Usage
  1. Data preparation (download our pre-processed data from: Hugging Face or Google Drive)
cd<data_path># then do whatever data preparation
  1. Command
# InstantSplat train and output video (no GT reference, render by interpolation) using the following command.# Users can place their data in the 'assets/examples/<scene_name>/images' folder and run the following command directly.
bash scripts/run_infer.sh
# InstantSplat train and evaluate (with GT reference) using the following command.
bash scripts/run_eval.sh
Acknowledgement
This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!
Citation
If you find our work useful in your research, please consider giving a star ⭐ and citing the following paper 📝.
@misc{fan2024instantsplat,
title={InstantSplat: Sparse-view Gaussian Splatting in Seconds},
author={Zhiwen Fan and Kairun Wen and Wenyan Cong and Kevin Wang and Jian Zhang and Xinghao Ding and Danfei Xu and Boris Ivanovic and Marco Pavone and Georgios Pavlakos and Zhangyang Wang and Yue Wang},
year={2024},
eprint={2403.20309},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
About
InstantSplat: Sparse-view SfM-free Gaussian Splatting in Seconds
instantsplat.github.io/
Resources
Readme
License
Activity
Custom properties
Stars
1.7k stars
Watchers
20 watching
Forks
156 forks
Report repository
Releases
Packages
Used by
Contributors
Languages