diff --git a/.gitattributes b/.gitattributes
index ec4a626fb..ec25dace3 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1 +1,3 @@
 *.pth filter=lfs diff=lfs merge=lfs -text
+pixi.lock merge=binary linguist-language=YAML linguist-generated=true
+Cargo.lock merge=binary linguist-generated=true
diff --git a/.github/workflows/pixi.yml b/.github/workflows/pixi.yml
new file mode 100644
index 000000000..aec399ffb
--- /dev/null
+++ b/.github/workflows/pixi.yml
@@ -0,0 +1,56 @@
+name: Validate pixi environment
+
+# Validate the default pixi environment on every push / PR.
+# Tests both supported platforms (linux-64 and osx-arm64).
+
+on:
+  push:
+  pull_request:
+
+permissions:
+  contents: read
+
+jobs:
+  validate-default-environment:
+    name: Validate default environment on ${{ matrix.platform }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - platform: linux-64
+            os: ubuntu-latest
+          - platform: osx-arm64
+            os: macos-14
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v6
+
+      - name: Install the default pixi environment
+        uses: prefix-dev/setup-pixi@v0.9.5
+        with:
+          pixi-version: latest
+          cache: false
+          environments: default
+
+      - name: List available pixi tasks
+        run: pixi task list
+
+      - name: Import project-specific third-party dependencies
+        run: |
+          pixi run python -c "
+          import gpmfstream
+          import open3d as o3d
+          import segmentation_models_pytorch as smp
+          print('Project-specific dependencies import OK')
+          "
+
+      - name: Import core project modules
+        run: |
+          pixi run python -c "
+          import video_utils
+          from sfm.model import SfMModel
+          from segmentation.model import SegmentationModel
+          print('Core project modules import OK')
+          "
diff --git a/.github/workflows/publish-container.yml b/.github/workflows/publish-container.yml
deleted file mode 100644
index 6e59263dc..000000000
--- a/.github/workflows/publish-container.yml
+++ /dev/null
@@ -1,87 +0,0 @@
-name: CI
-
-# This workflow builds a docker image and pushes it
-# to the github package registry
-
-on:
-  push:
-
-env:
-  # Use docker.io for Docker Hub if empty
-  REGISTRY: ghcr.io
-  # github.repository as <account>/<repo>
-  IMAGE_NAME: ${{ github.repository }}
-
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-      # This is used to complete the identity challenge
-      # with sigstore/fulcio when running outside of PRs.
-      id-token: write
-    outputs:
-      # The digest of the built image.
-      digest: ${{ steps.build-and-push.outputs.digest }}
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v3
-        with:
-          lfs: 'true'
-
-      # Install the cosign tool
-      # https://github.com/sigstore/cosign-installer
-      - name: Install cosign
-        uses: sigstore/cosign-installer@v3.3.0
-        with:
-          cosign-release: 'v2.2.2'
-
-      # Workaround: https://github.com/docker/build-push-action/issues/461
-      - name: Setup Docker buildx
-        uses: docker/setup-buildx-action@79abd3f86f79a9d68a23c75a09a9a85889262adf
-
-      # Login against a Docker registry
-      # https://github.com/docker/login-action
-      - name: Log into registry ${{ env.REGISTRY }}
-        uses: docker/login-action@28218f9b04b4f3f62068d7b6ce6ca5b26e35336c
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      # Extract metadata (tags, labels) for Docker
-      # https://github.com/docker/metadata-action
-      - name: Extract Docker metadata
-        id: meta
-        uses: docker/metadata-action@98669ae865ea3cffbcbaa878cf57c20bbf1c6c38
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-
-      # Build and push Docker image with Buildx
-      # https://github.com/docker/build-push-action
-      - name: Build and push Docker image
-        id: build-and-push
-        uses: docker/build-push-action@ac9327eae2b366085ac7f6a2d02df8aa8ead720a
-        with:
-          context: .
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-
-
-      # Sign the resulting Docker image digest.
-      # This will only write to the public Rekor transparency log when the Docker
-      # repository is public to avoid leaking data.  If you would like to publish
-      # transparency data even for private images, pass --force to cosign below.
-      # https://github.com/sigstore/cosign
-      - name: Sign the published Docker image
-        env:
-          COSIGN_EXPERIMENTAL: "true"
-        # This step uses the identity token to provision an ephemeral certificate
-        # against the sigstore community Fulcio instance.
-        run: echo "${{ steps.meta.outputs.tags }}" | xargs -I {} cosign sign --yes {}@${{ steps.build-and-push.outputs.digest }}
diff --git a/.gitignore b/.gitignore
index 841c77a94..15b422e74 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,8 @@
 checkpoints/
 
 # Video processing
+example_data/
+out*/
 src/tmp
 tmp
 *.MP4
diff --git a/Dockerfile b/Dockerfile
deleted file mode 100644
index 334cd5225..000000000
--- a/Dockerfile
+++ /dev/null
@@ -1,46 +0,0 @@
-FROM nvidia/cuda:11.8.0-base-ubuntu22.04
-
-# Set commit hash for gpmfstream repo as there are no versions
-ARG GPMFSTREAM_GIT_HASH=f1a9742
-ARG DEBIAN_FRONTEND=noninteractive
-
-# # Get the gpmfstream library
-RUN apt-get update && \
-    apt-get install -y git ffmpeg pipx python3-pip \
-    && pip3 install uv
-
-# RUN pip3 install poetry
-WORKDIR /app
-
-# Change to non-root user
-RUN groupadd mygroup --gid 1000 && \
-    useradd -m -U -s /bin/bash -G mygroup -u 1000 myuser && \
-    chown -R 1000:1000 /app && \
-    chmod -R 755 /app && \
-    mkdir /output /input && \
-    chown -R 1000:1000 /output /input /tmp && \
-    chmod -R o+w /input /output
-
-# Copy the model checkpoints and environment
-COPY --chown=1000:1000 segmentation_net.pth sfm_net.pth uv.lock pyproject.toml /app/
-
-# Build gpmfstream
-RUN git clone https://github.com/hovren/gpmfstream.git
-WORKDIR /app/gpmfstream
-RUN pip3 install pybind11 setuptools
-RUN git checkout ${GPMFSTREAM_GIT_HASH} \
-    && git submodule update --init
-RUN python3 setup.py bdist_wheel
-
-# Install dependencies
-WORKDIR /app
-RUN uv sync --no-dev --no-cache
-RUN uv pip install "./gpmfstream/dist/gpmfstream-0.5-cp310-cp310-linux_x86_64.whl"
-
-COPY --chown=1000:1000 src /app/src
-COPY --chown=1000:1000 example_inputs /app/example_inputs
-
-WORKDIR /app/src
-USER 1000
-
-ENTRYPOINT ["uv", "run", "python3", "reconstruct.py"]
diff --git a/README.md b/README.md
index 06ba7bf9f..4a7e7689a 100644
--- a/README.md
+++ b/README.md
@@ -1,156 +1,112 @@
 # Scalable 3D Semantic Mapping of Coral Reefs using Deep Learning
 
-This repository contais the source code for the Paper [Scalable 3D Semantic Mapping of Coral Reefs using Deep Learning](https://arxiv.org/abs/2309.12804).
+This repository contains the source code for the paper [Scalable 3D Semantic Mapping of Coral Reefs using Deep Learning](https://arxiv.org/abs/2309.12804).
 
-[The project page](https://josauder.github.io/deepreefmap/) contains updated information on the state of the project.
+The [project page](https://josauder.github.io/deepreefmap/) contains more information about DeepReefMap.
 
+## Installation
 
-## Quick start (docker)
-
-A docker image is available in this repository to run the code without
-installing any dependencies. 
+First install [Pixi](https://pixi.sh/). 
 
+Then clone this repository:
 ```bash
-docker run \
-    -v ./example_data/input_videos:/input \
-    -v ./output:/output \
-    ghcr.io/josauder/mee-deepreefmap \
-    --input_video=/input/GX_SINGLE_VIDEO.MP4 \
-    --timestamp=0-100 \
-    --out_dir=/output \
-    --fps=10
+git clone https://github.com/Tobias-Fischer/mee-deepreefmap.git
+cd mee-deepreefmap
+pixi install
 ```
 
-If using a GPU, make sure your docker runtime is configured to use the GPU
-(installing for example, `nvidia-container-toolkit` for NVIDIA GPUs), and run
-the docker image with the `--gpus all` flag.
-
-The Dockerfile used for building the above image is also included should
-you wish you build the image yourself.
+The Pixi environment installs the Python dependencies, the editable local package, `gpmfstream`, FFmpeg, Git-LFS, and the compiler tooling needed for the native extension.
 
-To build the docker image, use the following command:
+To fetch the Git-LFS model checkpoints used by reconstruction, run:
 
 ```bash
-docker build -t deepreefmap .
+pixi run _install
 ```
 
-**Note**: The above command assumes that the input video is located in
-`./example_data/input_videos`, the output will be saved to `./output`. The
-input video can be obtained from the example data located in the Zenodo archive
-described below.
+## Example Data
 
+The example videos are hosted on [Zenodo](https://zenodo.org/records/10624794). They are large, so the download is a separate cached Pixi task:
 
-## Installation
+```bash
+pixi run download-example-data
+```
 
-This repository depends on [gpmfstream](https://github.com/hovren/gpmfstream), which in turn depends on [gpmf-parser](https://github.com/gopro/gpmf-parser).
-To install gpmfstream, run the following:
+This extracts the archive to `example_data/`, including:
 
-```
-git clone https://github.com/hovren/gpmfstream.git
-cd gpmfstream
-git submodule update --init
-python3 setup.py install
+```text
+example_data/
+    input_videos/
+        GX_SINGLE_VIDEO.MP4
+        GX_VIDEO_1_OF_2.MP4
+        GX_VIDEO_2_OF_2.MP4
 ```
 
-### Poetry (recommended)
+## Running Reconstructions
 
-This repository uses [uv](https://docs.astral.sh/uv/) to manage the rest
-of the dependencies. To install the dependencies, run the following in the main
-repository directory:
+Run a single GoPro Hero 10 video:
 
 ```bash
-uv sync
+pixi run reconstruct \
+  example_data/input_videos/GX_SINGLE_VIDEO.MP4 \
+  0-367 \
+  out_single_video
 ```
 
-For any doubt, refer to the `Dockefile` for the complete method of installing
-the dependencies.
+Run a transect split across two GoPro Hero 10 videos:
 
-## Download Example Data and Pre-Trained Models:
+```bash
+pixi run reconstruct \
+  example_data/input_videos/GX_VIDEO_1_OF_2.MP4,example_data/input_videos/GX_VIDEO_2_OF_2.MP4 \
+  310-end,begin-100 \
+  out_two_videos
+```
 
-Pre-trained model checkpints and example input videos can be downloaded from the [Zenodo archive](https://zenodo.org/record/10624794).
+The `reconstruct` task depends on `_install`, so model checkpoints are pulled before the pipeline runs. Pixi also tracks the task inputs and outputs, so unchanged runs can be skipped.
 
-Checkpoints are also included in this repository with Git-LFS. Ensure you have Git-LFS installed before cloning the repository.
+## Other Cameras
 
-## Running 3D Reconstructions of GoPro Hero 10 Videos
+DeepReefMap currently targets GoPro Hero 10 videos. Other cameras can be used if you provide camera intrinsics in the same simplified EUCM JSON format as `example_inputs/intrinsics_eucm.json`.
 
-Simple usage: the input is one MP4 video taken with a GoPro Hero 10 camera, as well as the timestamps on when the transect begins and ends in the video (TODO: discuss format).
+The Pixi task exposes the common GoPro workflow. For custom reconstruction options such as `--intrinsics_file`, run the script inside the Pixi environment:
 
+```bash
+pixi run python src/reconstruct.py \
+  --input_video=example_data/input_videos/OTHER_CAMERA_VID.MP4 \
+  --timestamp=10-120 \
+  --out_dir=out_other_camera \
+  --intrinsics_file=example_inputs/intrinsics_eucm.json
 ```
-python3 reconstruct.py \
-    --input_video=<PATH_TO_VIDEO.MP4> \
-    --timestamp=<START_TIMESTAMP>-<END_TIMESTAMP> \
-    --out_dir=<OUTPUT_DIRECTORY>
-```
-
-Advanced usage: the GoPro Hero 10 camera cuts videos into 4GB chuns. If the transect is spread over two or more videos, the following command can be used to reconstruct the transect.
-
-```
-python3 reconstruct.py \
-    --input_video=<PATH_TO_VIDEO_1.MP4>,<PATH_TO_VIDEO_2.MP4> \
-    --timestamp=<START_TIMESTAMP_1>-end,begin-<END_TIMESTAMP_2> \
-    --out_dir=<OUTPUT_DIRECTORY>
-```
-
-## Running 3D Reconstructions of Videos from other Cameras
 
-This repository, for now, supports the GoPro Hero 10 Camera. If you want to use a different camera, be sure to provide the correct camera intrinsics as `intrinsics.json`, which are passed as a command line argument to any other scripts. For now, the intrinsics follow a simplified UCM format, with the focal lengths `fx, fy` in pixels, the focal point `cx, cy` in pixels, and the `alpha` value to account for distortion, which is set to zero in the default case to assume linear camera intrinsics.
+## Training
 
-## Training the 3D Reconstruction Network on Your Own Data
-
-To train the 3D reconstruction data on your own data, use
+Train the SfM network:
 
+```bash
+pixi run train-sfm <PATH_TO_DATA> <WANDB_EXPERIMENT_NAME>
 ```
-sfm/train_sfm.py
-    --data <PATH_TO_DATA> \
-    --checkpoint <PATH_TO_PRETRAINED_CHECKPOINT> \
-    --name <NAME_OF_WANDB_EXPERIMENT>
-````
 
-Where your data should be a directory of the following structure (same as KITTI VO Dataset):
+The SfM dataset should follow the KITTI VO-style layout:
 
-```
+```text
 train.txt
 val.txt
 sequence1/
     000001.jpg
     000002.jpg
-    ...
-sequence1/
+sequence2/
     000001.jpg
     000002.jpg
-    ...
-sequence3/
-    000001.jpg
-    000002.jpg
-    ...
 ```
 
-With `train.txt` containing, for example
+Train the semantic segmentation network:
 
+```bash
+pixi run train-segmentation <PATH_TO_DATA> <WANDB_EXPERIMENT_NAME>
 ```
-sequence1
-sequence2
-```
-And `val.txt` containing, for example
-
-```
-sequence3
-```
-
-## Training the Semantic Segmentation Network on Your Own Data
-
-For training the segmentation model, use
 
-```
-python3 train_segmentation.py \
-    --data <PATH_TO_DATA> \
-    --checkpoint <PATH_TO_PRETRAINED_CHECKPOINT> \
-    --test_splits <TEST_SCENE1>,<TEST_SCENE2> \
-    --name <NAME_OF_WANDB_EXPERIMENT>
-```
+The segmentation dataset should contain class metadata and scene folders:
 
-Where your data should be a directory with the following structure:
-```
+```text
 data/
     classes.json
     colors.json
@@ -159,16 +115,20 @@ data/
         image_0.png
         image_0_seg.npy
         image_0_poly.npy
-        image_1.png
-        image_1_seg.npy
-        image_1_poly.npy
-        ...
-    scene_2/
-        image_0.png
-        image_0_seg.npy
-        image_0_poly.npy
-        ...
-    ...
 ```
 
-Following the example dataset from the [Zenodo archive](https://zenodo.org/record/10624794).
\ No newline at end of file
+## Pixi Tasks
+
+```bash
+pixi task list
+```
+
+Available tasks:
+
+```text
+_install               Fetch Git-LFS model checkpoints.
+download-example-data  Download and extract the Zenodo example videos.
+reconstruct            Run the reconstruction pipeline.
+train-sfm              Train the SfM model.
+train-segmentation     Train the segmentation model.
+```
diff --git a/pixi.lock b/pixi.lock
new file mode 100644
index 000000000..94fe3f897
--- /dev/null
+++ b/pixi.lock
@@ -0,0 +1,11488 @@
+version: 7
+platforms:
+- name: linux-64
+- name: osx-arm64
+environments:
+  cuda:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    indexes:
+    - https://pypi.org/simple
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.13.5-py311h55b9665_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.15.3-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aom-3.9.1-hac33072_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/assimp-6.0.5-h6b1a590_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.5.0-py311h6b1f9c4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45.1-default_h4852527_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_102.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-he440d0b_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py311h66f275b_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brunsli-0.1-hd1e3526_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-blosc2-3.0.2-hc31b594_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-he90730b_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/charls-2.4.3-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cli11-2.6.2-h54a6638_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/compilers-1.11.0-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_19.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py311h724c32c_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.9.86-ha770c72_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.9.79-h5888daf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cuobjdump-12.9.82-hffce074_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-12.9.79-h676940d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvcc-tools-12.9.86-he02047a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvdisasm-12.9.88-hffce074_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvrtc-12.9.86-hecca717_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvtx-12.9.79-hecca717_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-nvvm-tools-12.9.86-h4bc722e_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hac629b4_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/dav1d-1.2.1-hd590300_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.4.0-hecca717_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/eigen-abi-5.0.1.80-hf414acd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/embree-4.4.1-hcc6314f_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ffmpeg-8.1.1-gpl_ha577776_101.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fmt-12.1.0-hff5e90c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.17.1-h27c8c51_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.62.1-py311h3778330_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.11.0-h9bea470_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/frozenlist-1.7.0-py311h52bc045_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_19.conda
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+  - black ; extra == 'test'
+  - mock ; extra == 'test'
+  - toml ; extra == 'test'
+  - pyyaml ; extra == 'test'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-subtests ; extra == 'test'
+  requires_python: '>=3.6'
diff --git a/pixi.toml b/pixi.toml
new file mode 100644
index 000000000..88d29ab69
--- /dev/null
+++ b/pixi.toml
@@ -0,0 +1,153 @@
+[workspace]
+name = "mee-deepreefmap"
+version = "1.1.0"
+description = "Scalable 3D Semantic Mapping of Coral Reefs using Deep Learning"
+channels = ["conda-forge"]
+platforms = ["linux-64", "osx-arm64"]
+
+# ---------------------------------------------------------------------------
+# Tasks – mirror the workflows described in README.md
+# ---------------------------------------------------------------------------
+
+[tasks.reconstruct]
+# Run a 3D reconstruction from a GoPro Hero 10 video.
+#   input_video : path to the MP4 file (comma-separated for multi-clip transects)
+#   timestamp   : begin-end marker, e.g. "0-100" or "0:23-end,begin-1:44"
+#   out_dir     : output directory (default: "out")
+#   fps         : frame extraction rate (default: 10 for the example data)
+args = [
+  "input_video",
+  "timestamp",
+  { arg = "out_dir", default = "out" },
+  { arg = "fps", default = "10" },
+]
+cmd = "python src/reconstruct.py --input_video={{ input_video }} --timestamp={{ timestamp }} --out_dir={{ out_dir }} --tmp_dir={{ out_dir }}/tmp --fps={{ fps }} --sfm_checkpoint=sfm_net.pth --segmentation_checkpoint=segmentation_net.pth --intrinsics_file=example_inputs/intrinsics_eucm.json --class_to_label_file=example_inputs/class_to_label.json --class_to_color_file=example_inputs/class_to_color.json"
+depends-on = ["_install"]
+env = { MPLCONFIGDIR = ".pixi/matplotlib", TORCH_HOME = ".pixi/torch", HF_HOME = ".pixi/huggingface" }
+inputs = [
+  "src/*.py",
+  "src/segmentation/*.py",
+  "src/sfm/*.py",
+  "example_inputs/*.json",
+  "sfm_net.pth",
+  "segmentation_net.pth",
+  "{{ input_video }}",
+]
+outputs = [
+  "{{ out_dir }}/class_to_color.json",
+  "{{ out_dir }}/percentage_covers.json",
+  "{{ out_dir }}/point_cloud_tsdf.csv",
+  "{{ out_dir }}/results.npy",
+]
+
+[tasks.download-example-data]
+# Download and extract the Zenodo example videos under example_data/.
+cmd = "python scripts/download_example_data.py"
+inputs = [
+  "scripts/download_example_data.py",
+]
+outputs = [
+  "example_data/input_videos/GX_SINGLE_VIDEO.MP4",
+  "example_data/input_videos/GX_VIDEO_1_OF_2.MP4",
+  "example_data/input_videos/GX_VIDEO_2_OF_2.MP4",
+]
+
+[tasks.train-sfm]
+# Train the SfM (depth-estimation) network on your own data.
+#   data : path to the dataset directory (KITTI VO layout with train.txt / val.txt)
+#   name : experiment name logged to Weights & Biases
+args = ["data", "name"]
+cmd = "python src/sfm/train_sfm.py --data={{ data }} --name={{ name }}"
+inputs = [
+  "src/sfm/*.py",
+  "{{ data }}/**/*",
+]
+outputs = [
+  "{{ name }}_best.pth",
+  "{{ name }}_last.pth",
+]
+
+[tasks.train-segmentation]
+# Train the semantic segmentation network on your own data.
+#   data : path to the dataset directory (classes.json, colors.json, scene folders)
+#   name : experiment name logged to Weights & Biases
+args = ["data", "name"]
+cmd = "python src/segmentation/train_segmentation_model.py --data={{ data }} --name={{ name }}"
+inputs = [
+  "src/segmentation/*.py",
+  "{{ data }}/**/*",
+]
+outputs = [
+  "{{ name }}.pth",
+]
+
+[tasks._install]
+# Fetch Git-LFS managed checkpoint files used by the reconstruction workflow.
+cmd = "git-lfs pull"
+inputs = [
+  ".gitattributes",
+]
+outputs = [
+  "sfm_net.pth",
+  "segmentation_net.pth",
+]
+
+# ---------------------------------------------------------------------------
+# Base (platform-independent) conda dependencies
+# ---------------------------------------------------------------------------
+[dependencies]
+python          = ">=3.10,<3.12"
+addict          = "*"
+h5py            = "*"
+matplotlib      = "*"
+numpy           = "*"
+open3d          = "*"
+pandas          = "*"
+pillow          = "*"
+pytorch         = "*"
+torchvision     = "*"
+scikit-image    = "*"
+scikit-learn    = "*"
+scipy           = "*"
+tqdm            = "*"
+wandb           = "*"
+ffmpeg          = "*"          # provides ffmpeg + ffprobe in PATH
+pybind11        = "*"          # required to build gpmfstream C extension
+setuptools      = "*"
+compilers       = "*"          # gcc (linux) / clang (mac) for C-extension builds
+pip             = "*"
+git             = "*"
+git-lfs         = "*"
+# Encoder/decoder library for semantic segmentation models.
+segmentation-models-pytorch = "*"
+
+# ---------------------------------------------------------------------------
+# PyPI-only dependencies (no conda-forge package available)
+# ---------------------------------------------------------------------------
+[pypi-options]
+no-build-isolation = true
+
+[pypi-dependencies]
+# Install this project's own packages (sfm, segmentation, video_utils, …)
+# editably so scripts can import them without path manipulation.
+mee-deepreefmap = { path = ".", editable = true }
+
+# GoPro Metadata Format stream parser (C extension; uses git submodules).
+# Pixi will clone the repo and initialise submodules automatically.
+# Pinned because the upstream repository does not publish versioned releases.
+gpmfstream = { git = "https://github.com/Tobias-Fischer/gpmfstream", rev = "ab23cfd06f65a04c3de835bba3747f6aefa6a40d" }
+
+[feature.cuda.target.linux-64.dependencies]
+pytorch-gpu = "*"
+
+[feature.cuda.system-requirements]
+cuda = "12"
+
+# ===========================================================================
+# Environments
+# ===========================================================================
+[environments]
+# Default: CPU on Linux, MPS-capable on macOS Apple Silicon.
+default = { solve-group = "default" }
+# GPU/CUDA: Linux only. Adds the conda-forge GPU selector package.
+cuda    = { features = ["cuda"], solve-group = "cuda" }
diff --git a/pyproject.toml b/pyproject.toml
index af97ead45..a6d112633 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,25 +1,34 @@
+[build-system]
+requires = ["setuptools>=64"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.package-dir]
+"" = "src"
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
 [project]
 name = "mee-deepreefmap"
 version = "1.1.0"
 description = "Scalable 3D Semantic Mapping of Coral Reefs using Deep Learning"
 readme = "README.md"
 requires-python = ">=3.10, <3.12"
-authors = ["Jonathan Sauder <josaudergh@gmail.com>"]
+authors = [{name = "Jonathan Sauder", email = "josaudergh@gmail.com"}]
 dependencies = [
-    "addict==2.4.0",
-    "h5py==3.7.0",
-    "matplotlib==3.5.3",
-    "numpy==1.26.4",
-    "open3d==0.16.0",
-    "pandas==1.4.4",
-    "pillow==9.1",
-    "scikit-image==0.24.0",
-    "scikit-learn==1.5.2",
-    "scipy==1.14.1",
-    "segmentation-models-pytorch==0.3.3",
-    "torch==2.0.1",
-    "torchaudio==2.0.2",
-    "torchvision==0.15.2",
-    "tqdm==4.64.1",
-    "wandb==0.13.7",
+    "addict>=2.4.0",
+    "h5py>=3.7.0",
+    "matplotlib>=3.5.3",
+    "numpy>=1.26.4",
+    "open3d>=0.16.0",
+    "pandas>=1.4.4",
+    "pillow>=9.1",
+    "scikit-image>=0.24.0",
+    "scikit-learn>=1.5.2",
+    "scipy>=1.14.1",
+    "segmentation-models-pytorch>=0.3.3",
+    "torch>=2.0.1",
+    "torchvision>=0.15.2",
+    "tqdm>=4.64.1",
+    "wandb>=0.13.7",
 ]
diff --git a/scripts/download_example_data.py b/scripts/download_example_data.py
new file mode 100644
index 000000000..53ed31eac
--- /dev/null
+++ b/scripts/download_example_data.py
@@ -0,0 +1,79 @@
+from __future__ import annotations
+
+import urllib.request
+import zipfile
+from pathlib import Path
+
+
+URL = "https://zenodo.org/records/10624794/files/example_data.zip?download=1"
+ROOT = Path(__file__).resolve().parents[1]
+DESTINATION = ROOT / "example_data"
+ARCHIVE = DESTINATION / "example_data.zip"
+PARTIAL_ARCHIVE = ARCHIVE.with_suffix(".zip.part")
+EXPECTED_OUTPUTS = [
+    DESTINATION / "input_videos" / "GX_SINGLE_VIDEO.MP4",
+    DESTINATION / "input_videos" / "GX_VIDEO_1_OF_2.MP4",
+    DESTINATION / "input_videos" / "GX_VIDEO_2_OF_2.MP4",
+]
+
+
+def format_gib(size: int) -> str:
+    return f"{size / 1024**3:.1f} GiB"
+
+
+def download() -> None:
+    with urllib.request.urlopen(URL) as response, PARTIAL_ARCHIVE.open("wb") as archive:
+        total = int(response.headers.get("Content-Length", "0"))
+        downloaded = 0
+        next_report = 0
+
+        while True:
+            chunk = response.read(1024 * 1024)
+            if not chunk:
+                break
+            archive.write(chunk)
+            downloaded += len(chunk)
+
+            if downloaded >= next_report:
+                if total:
+                    print(f"Downloaded {format_gib(downloaded)} / {format_gib(total)}", flush=True)
+                else:
+                    print(f"Downloaded {format_gib(downloaded)}", flush=True)
+                next_report = downloaded + 100 * 1024 * 1024
+
+    PARTIAL_ARCHIVE.replace(ARCHIVE)
+
+
+def normalize_extracted_layout() -> None:
+    nested = DESTINATION / "example_data"
+    if not nested.exists():
+        return
+
+    for path in nested.iterdir():
+        destination = DESTINATION / path.name
+        if destination.exists():
+            continue
+        path.rename(destination)
+
+
+def main() -> int:
+    DESTINATION.mkdir(exist_ok=True)
+
+    if all(path.exists() for path in EXPECTED_OUTPUTS):
+        print("Example data already exists; nothing to download.")
+        return 0
+
+    print(f"Downloading example data to {ARCHIVE}")
+    download()
+
+    print(f"Extracting {ARCHIVE} to {DESTINATION}")
+    with zipfile.ZipFile(ARCHIVE) as archive:
+        archive.extractall(DESTINATION)
+    normalize_extracted_layout()
+
+    ARCHIVE.unlink()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/segmentation/model.py b/src/segmentation/model.py
index 27e95bde3..d967702ef 100644
--- a/src/segmentation/model.py
+++ b/src/segmentation/model.py
@@ -12,7 +12,7 @@
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-SegmentationModel = lambda classes: smp.DeepLabV3Plus(encoder_name="resnext50_32x4d", classes=classes)
+SegmentationModel = lambda classes: smp.DeepLabV3Plus(encoder_name="resnext50_32x4d", encoder_weights=None, classes=classes)
 
 class SegmentationDataset(torch.utils.data.Dataset):
     def __init__(self, image_files, label_files, output_size, imagenet_normalization=False):
diff --git a/src/sfm/model.py b/src/sfm/model.py
index 53c5a86e1..8f13edb5d 100644
--- a/src/sfm/model.py
+++ b/src/sfm/model.py
@@ -8,7 +8,7 @@ class SfMModel(nn.Module):
     def __init__(self,in_channels=3):
         super().__init__()
 
-        self.depth_net = smp.DeepLabV3Plus(in_channels=in_channels, encoder_name="resnext50_32x4d", encoder_weights='swsl', activation=None)
+        self.depth_net = smp.DeepLabV3Plus(in_channels=in_channels, encoder_name="resnext50_32x4d", encoder_weights=None, activation=None)
         self.pose_reduction = nn.Sequential(
             nn.Conv2d(2048, 512, (1, 1)), nn.ReLU(),  nn.BatchNorm2d(512),
         )
diff --git a/src/sfm/utils.py b/src/sfm/utils.py
index 805c5fc31..3feff904c 100644
--- a/src/sfm/utils.py
+++ b/src/sfm/utils.py
@@ -76,7 +76,8 @@ def get_depths_and_poses(encoder, segmentation_head, decoder, pose_decoder, imag
             features.append(features_[i][b:])
 
 
-    depths = segmentation_head(decoder(*features)).reshape(b, l, 1, h, w)
+    decoder_output = decoder(features)
+    depths = segmentation_head(decoder_output).reshape(b, l, 1, h, w)
 
     _, c_feat, h_feat, w_feat = features[-1].shape
     last_feat = features[-1]
diff --git a/uv.lock b/uv.lock
deleted file mode 100644
index 5735d526a..000000000
--- a/uv.lock
+++ /dev/null
@@ -1,1800 +0,0 @@
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-requires-python = ">=3.10, <3.12"
-resolution-markers = [
-    "sys_platform != 'linux'",
-    "sys_platform == 'linux'",
-]
-
-[[package]]
-name = "addict"
-version = "2.4.0"
-source = { registry = "https://pypi.org/simple" }
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-name = "attrs"
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