vipe

ViPE: Video Pose Engine for Geometric 3D Perception

TL;DR: ViPE is a useful open-source spatial AI tool for annotating camera poses and dense depth maps from raw videos!

ViPE estimates camera intrinsics, camera motion, and dense near-metric depth maps from unconstrained raw videos, including pinhole, wide-angle, and 360-degree panorama footage.

News

• 2026/11: 🚀🚀🚀 Released ViPE-Long: Added long-sequence pipeline (metrictrail) for streaming long videos reconstruction, with metric depth and local optimization as key features. Uses the MoGe v2 (moge2-l) keyframe-depth prior by default.
• 2026/09: Added long-sequence SLAM (pose_only_long) for arbitrarily long videos, with GPU/CPU memory bounded by a sliding keyframe window instead of growing with video length. Uses the MoGe v2 (moge2-l) keyframe-depth prior by default.
• 2026/06: 🚀🚀🚀 Released ViPE 1.2.0: 2.7x speed-up with no loss of accuracy, enabled by CUDA fused kernels, model and pipeline caching, prefetching, and other optimizations.
• 2026/05: Merged Panorama estimation pipeline & bump release version to 1.0.0.
• 2026/01: Integration with Depth-Anything 3 for depth estimation (use dav3 pipeline).
• 2025/10: Add support to run on wide-angle videos.
• 2025/09: Add support to run Lyra pipeline.
• 2025/08: Initial release of ViPE.

Installation

# From PyPI
pip install nvidia-vipe

# From source (conda for CUDA/native deps, uv for the Python env)
conda env create -f envs/cu128.yml
conda activate cu128
uv sync
uv run vipe infer YOUR_VIDEO.mp4

See docs/installation.md for details (dev/docs dependency groups, etc).

Long-Sequence Videos

For videos too long for the default pipeline's fixed-size keyframe buffer, use the pose_only_long pipeline: it retires old keyframes to a compact trajectory ledger as it streams, so GPU and CPU memory stay bounded (roughly constant, not growing with video length) regardless of how many frames the video has. It uses the MoGe v2 (moge2-l) keyframe-depth prior by default.

# Single video
uv run vipe infer YOUR_LONG_VIDEO.mp4 -p pose_only_long -o vipe_results/

# A folder of videos: every .mp4 in the folder is processed in turn, through a single
# loaded model instance, writing each video's trajectory to vipe_results/pose/<name>.npz
uv run vipe infer YOUR_VIDEO_FOLDER/ -p pose_only_long -o vipe_results/

MetricTrail

MetricTrail is a streaming metric geometry system that combines pretrained spatial priors with bounded local geometric optimization to recover accurate camera trajectories and dense 3D reconstruction from long videos. To use metrictrail, build MetricTrail's DPVO kernels after the installation of ViPE.

Build

uv pip install yacs numba   # DPVO runtime requirements (not ViPE dependencies)
uv pip install xformers==0.0.33.post1  # MoGe-2's DINOv2 attention
python -m vipe.metrictrail.build       # cuda_corr, cuda_ba, lietorch_backends
python -m vipe.metrictrail.build --stereo   # also the optional stereo DPVO kernels
python -m vipe.metrictrail.build --dry-run  # print the plan only

Point CUDA_HOME at the conda environment's toolkit (export CUDA_HOME=$CONDA_PREFIX after conda activate cu128) when the shell presets another CUDA, so all extensions use nvcc 12.8.

The sources travel with ViPE's sdist (MANIFEST.in) and are package data of vipe.metrictrail, so the build also works from an installed (non-editable) ViPE.

Weights

MetricTrail weights are obtained separately, with a Hugging Face release planned under the NVIDIA License. Set pipeline.slam.weights or VIPE_METRICTRAIL_DPVO_CHECKPOINT to the DINO-DPVO checkpoint.

weight	setting	default
MoGe-2 ViT-L	ViPE's own	https://github.com/microsoft/moge
DINO-DPVO	pipeline.slam.weights	https://huggingface.co/nvidia/MetricTrail
DINOv3 ViT-S/16	pipeline.slam.dinov3_weights	https://github.com/facebookresearch/dinov3
DINO SALAD	External loop-candidate generation	https://github.com/serizba/salad/

For SALAD candidates, follow the separate installation and export instructions. SALAD and its checkpoints stay in that external environment; they are not bundled with ViPE.

Optional loop-closure dependency

Install the pose-graph dependency in the ViPE environment, separately from SALAD:

python -m pip install pypose==0.6.8
# Alternatively, when installing this checkout:
python -m pip install '.[metrictrail-lc]'

Loop closure uses PyPose on CPU for SE(3) operations and autograd, and SciPy for sparse linear solves. PyPose is loaded only for loop-closure optimization; local tracking does not require it. See the file-based loop-closure workflow.

Quick start

The demo video (assets/examples/metrictrail_demo.mp4): the in-the-wild defaults, i.e. a 280x504 cover-crop at the video's own frame rate, RGB-only SharedK calibration, local tracking.

python run.py pipeline=metrictrail streams.base_path=assets/examples/metrictrail_demo.mp4 \
    pipeline.output.path=vipe_results/metrictrail_demo
# -> vipe_results/metrictrail_demo/{pose,intrinsics,depth}/metrictrail_demo.*  and the run record
# vipe_results/metrictrail_demo/vipe/metrictrail_demo_metrictrail.json 

# point cloud export
python -m vipe.metrictrail.pointcloud vipe_results/metrictrail_demo --name metrictrail_demo \
    --video assets/examples/metrictrail_demo.mp4 --filter strict
# -> vipe_results/metrictrail_demo/pointcloud/metrictrail_demo.ply

KITTI odometry, one sequence, uncalibrated, with loop closure (frames prepared at 280x504; data layout and every benchmark in configs/slam/README_metrictrail.md):

python run.py pipeline=metrictrail [email protected]=metrictrail_local \
    streams=frame_dir_stream streams.base_path=data/KITTI/rgb/02 \
    pipeline.init.input_raster.policy=unchanged \
    pipeline.init.calibration.allow_unstable=true \
    pipeline.output.path=vipe_results/kitti

# After exporting candidate JSON for the saved trajectory:
python -m vipe.metrictrail.optional.loop_closure vipe_results/kitti --name 02 --preset kitti \
    --candidates /path/to/02-loops.json
# vipe_results/kitti/pose/02.npz (before loop closure), vipe_results/kitti/pose_lc/02.npz (after)

Scoring is a separate package on the metrictrail_eval branch (its README lists every dataset and how to plot the aligned trajectories, python -m metrictrail_eval.plot):

git worktree add ../vipe-metrictrail-eval metrictrail_eval
cd ../vipe-metrictrail-eval/metrictrail_eval
python -m metrictrail_eval.score --dataset kitti --pred-format vipe --pred <repo>/vipe_results/kitti \
    --gt-root <KITTI GT, e.g. data/KITTI/prepared_280x504> --cases 02 --allow-partial --stage LC  # --stage local: before LC

Refer to MetricTrail for detailed explanations and further usages.

License

This project will download and install additional third-party models and softwares. Note that these models or softwares are not distributed by NVIDIA. Review the license terms of these models and projects before use. NVIDIA-authored source is released under the Apache License 2.0. Incorporated third-party code retains its applicable terms, including Unik3D (BY-NC-SA 4.0), DPVO (MIT), and LieTorch (BSD-3-Clause). See THIRD_PARTY_LICENSES.md and NOTICE.