setup https://github.com/graphdeco-inria/gaussian-splatting on Windows 11

gaussian-splatting-win11-init.ps1

# INFO: Run this command on graphdeco-inria/gaussian-splatting project root

# check commands
Get-Command "conda" || { Write-Error "conda is required" && Exit }
Get-Command "ninja" || { Write-Error "ninja is required" && Exit }

# setup CUDA 11.8
(nvcc -V | Select-String -Pattern "11.8") || { Write-Error "CUDA 11.8 is required" && Exit }
$env:CUDA_HOME = (Get-Command nvcc).Path | Split-Path -Parent | Split-Path -Parent

# setup Visual Studio 2022
# pass env-vars to powershell
cmd.exe /c "call `"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat`" && set > %temp%\vars.txt"
Get-Content "${env:temp}\vars.txt" | Foreach-Object {
   if ($_ -match "^(.*?)=(.*)$") {
      Set-Content "env:\$($matches[1])" $matches[2]
   }
}
# force distutils to use Visual Studio
$env:DISTUTILS_USE_SDK = 1

# setup conda
if (conda env list | Select-String -Pattern "gaussian_splatting") {
   conda env update
}
else {
   conda env create --file environment.yml
}
conda activate gaussian_splatting

# install binaries
New-Item -ItemType Directory -Force -Path bin
# COLMAP
Invoke-WebRequest "https://github.com/colmap/colmap/releases/latest/download/COLMAP-3.8-windows-cuda.zip" -OutFile "${env:temp}/COLMAP-3.8-windows-cuda.zip"
Expand-Archive -Path "${env:temp}/COLMAP-3.8-windows-cuda.zip" -DestinationPath bin -Force
Remove-Item "${env:temp}/COLMAP-3.8-windows-cuda.zip"
# Viewer
Invoke-WebRequest "https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip" -OutFile "${env:temp}/viewers.zip"
Expand-Archive -Path "${env:temp}/viewers.zip" -DestinationPath bin/viewers -Force
Remove-Item "${env:temp}/viewers.zip"

Write-Output "Setup complete. Next steps:"
Write-Output "1. $ ffmpeg -i { { input.mp4 } } -r { { frame rate } } { { images_dir } }/input/frame%04d.png"
Write-Output "2. $ python convert.py --colmap_executable bin/COLMAP-3.8-windows-cuda/COLMAP.bat -s { { images_dir } }"
Write-Output "3. $ python train.py -s { { images_dir } }"
Write-Output "4. $ ./bin/viewers/bin/SIBR_gaussianViewer_app.exe -m ./output/ { { newly trained model } }"