# princeton-vl/DPVO

Deep Patch Visual Odometry/SLAM

Repository: https://github.com/princeton-vl/DPVO
Canonical: https://ross.abutalabs.com/products/dpvo
Language: C++
License: MIT
License Family: permissive
Last push: 2024-10-12T23:16:08+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1486, "days_push": 690, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1108, forks 167 (observed 2026-08-28T04:03:36.696871+00:00)

## What it is
DPVO is a deep learning-based visual odometry and SLAM system that estimates camera trajectories from video or image sequences using patch-based neural tracking. It implements the Deep Patch Visual Odometry (NeurIPS 2023) and Deep Patch Visual SLAM (ECCV 2024) papers from Princeton Vision Lab.

## Use cases
- estimate camera trajectory from a video
- run visual SLAM on image sequences
- compute camera poses for 3D reconstruction
- track camera motion in monocular video
- benchmark visual odometry on datasets like TUM or EuRoC
- build AR or robotics localization pipelines

## When to choose
- you need learning-based visual odometry with strong accuracy on monocular video
- you want a research-grade SLAM system with GPU acceleration and a real-time Pangolin viewer
- you need to reproduce or extend the DPVO/DPV-SLAM papers

## When to avoid
- you need a CPU-only solution, since it requires CUDA GPUs
- you need a production-hardened SLAM stack with long-term support
- you need RGB-D or multi-camera sensor fusion out of the box

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, graphics
- domain: computer-vision, robotics, autonomous-vehicles, deep-learning
- platform: python, cpp
- tags: visual-odometry, slam, camera-pose-estimation, cuda, research-code, 3d-reconstruction, linux, gpu, docker

## Member repositories
- princeton-vl/DPVO (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:36.696871+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:43:51.222371+00:00, confidence not recorded.
  - readme: https://github.com/princeton-vl/DPVO (fetched 2026-08-28T04:03:36.696871+00:00, sha e874cfd971b4)
- Data as of 2026-08-30T08:39:29.467469+00:00.