# nianticlabs/monodepth2

[ICCV 2019] Monocular depth estimation from a single image

Repository: https://github.com/nianticlabs/monodepth2
Canonical: https://ross.abutalabs.com/products/monodepth2
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: depth-estimation, neural-network, deep-learning, self-supervision, pytorch, monodepth, computer-vision
Last push: 2024-08-10T22:20:36+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": 2680, "days_push": 753, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4497, forks 987 (observed 2026-08-28T04:08:51.430886+00:00)

## What it is
Monodepth2 is the reference PyTorch implementation of the ICCV 2019 paper 'Digging into Self-Supervised Monocular Depth Prediction'. It trains and runs neural networks that estimate depth (disparity) from a single monocular image using self-supervision, with pretrained models provided.

## Use cases
- estimate depth from a single image
- predict monocular disparity with a neural network
- train a self-supervised depth estimation model
- run monocular depth estimation on KITTI-style driving footage
- get metric depth from stereo-trained models
- reproduce ICCV 2019 monodepth2 research results

## When to choose
- you need single-image depth estimation with pretrained PyTorch models
- you want to reproduce or build on the Monodepth2 paper
- you need a self-supervised depth prediction baseline for research

## When to avoid
- you need real-time depth on mobile or embedded devices
- you need a maintained production library with modern PyTorch support
- you need commercial usage - the code is non-commercial only
- you need multi-frame or video depth estimation

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python
- tags: depth-estimation, monocular-depth, self-supervised-learning, pytorch, disparity, research-code, iccv-2019, kitti, linux, gpu

## Member repositories
- nianticlabs/monodepth2 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:51.430886+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 2026-08-29T18:20:26.713339+00:00, confidence not recorded.
  - readme: https://github.com/nianticlabs/monodepth2 (fetched 2026-08-28T04:08:51.430886+00:00, sha 25d356cffcf4)
- Data as of 2026-08-30T08:39:29.467469+00:00.