Add Pixi as an option for installation (#3130)

* pixi installation optiont

* chore: Update .gitignore to include /third_party and include pixi lock file

* feat: Update download and training commands for dozer data

The commit updates the download and training commands for the dozer data in the `pixi.toml` file. It renames the `download-sample-data` command to `download-dozer-data` and modifies the `train-example-splat` and `train-example-nerf` commands to include the dozer data as a dependency.

This makes it easy to run an example training for both nerfacto and splatfacto

* update to include range between 3.8 -3.10

* Add Pixi installation instructions to the installation.md file

---------

Co-authored-by: maturk <30566358+maturk@users.noreply.github.com>

.gitignore

@@ -188,3 +188,6 @@ camera_paths/
 */**/.DS_Store
 */**/._.DS_Store
 
+# pixi environments
+.pixi
+/third_party

docs/quickstart/installation.md

@@ -122,6 +122,35 @@ pip install -e .[dev]
 pip install -e .[docs]
 ```
 
+## Using Pixi
+[Pixi](https://pixi.sh/latest/) is a fast software package manager built on top of the existing conda ecosystem. Spins up development environments quickly on Windows, macOS and Linux. (Currently only linux is supported for nerfstudio)
+
+### Prerequisites
+Make sure to have pixi installed, detailed instructions [here](https://pixi.sh/latest/)
+
+TLDR for linux:
+
+```bash
+curl -fsSL https://pixi.sh/install.sh | bash
+```
+
+### Install Pixi Environmnent 
+After Pixi is installed, you can run
+```bash
+pixi run post-install
+pixi shell
+```
+This will install all enviroment dependancies including colmap, tinycudann and hloc, and the active the conda environment
+
+you could also run
+
+```bash
+pixi run post-install
+pixi run train-example-nerf
+```
+
+to download an example dataset and run nerfacto straight away
+
 ## Use docker image
 
 Instead of installing and compiling prerequisites, setting up the environment and installing dependencies, a ready to use docker image is provided.

pixi.toml

@@ -0,0 +1,36 @@
+[project]
+name = "nerfstudio"
+version = "1.0.3"
+description = "All-in-one repository for state-of-the-art NeRFs"
+channels = ["nvidia/label/cuda-11.8.0", "nvidia", "conda-forge", "pytorch"]
+platforms = ["linux-64"]
+
+[system-requirements]
+libc = { family="glibc", version="2.30" }
+
+[tasks]
+# || operator in pixi allows running a command after only if the command before || fails
+tcnn-install = "python -c 'import tinycudann as tcnn' || python -m pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch"
+make-third_party-dir = {cmd="ls third_party || mkdir third_party"}
+clone-hloc = {cmd="ls Hierarchical-Localization || git clone --recursive https://github.com/cvg/Hierarchical-Localization/", cwd = "third_party", depends_on=["make-third_party-dir"] }
+hloc-install = {cmd="python -m pip install -e .", cwd = "third_party/Hierarchical-Localization" , depends_on=["clone-hloc"], outputs=["third_party/Hierarchical-Localization/hloc.egg-info/PKG-INFO"]}
+post-install = {cmd="pwd", depends_on=["hloc-install", "tcnn-install"]}
+
+download-dozer-data = {cmd="ls data/nerfstudio/dozer || ns-download-data nerfstudio --capture-name dozer"}
+train-example-splat = {cmd="ns-train splatfacto --data data/nerfstudio/dozer/", depends_on=["post-install", "download-dozer-data"]}
+train-example-nerf = {cmd="ns-train nerfacto --data data/nerfstudio/dozer/", depends_on=["post-install", "download-dozer-data"]}
+
+
+[dependencies]
+python = ">=3.8,<3.11"
+pip = ">=24.0,<25"
+cuda = {version = "*", channel="nvidia/label/cuda-11.8.0"}
+pytorch-cuda = {version = "11.8.*", channel="pytorch"}
+pytorch = {version = ">=2.2.0,<2.3", channel="pytorch"}
+torchvision = {version = ">=0.17.0,<0.18", channel="pytorch"}
+pyarrow = ">=15.0.2,<15.1"
+colmap = ">=3.9.1,<3.10"
+
+
+[pypi-dependencies]
+nerfstudio = { path = ".", editable = true}