# TRI-ML/packnet-sfm

TRI-ML Monocular Depth Estimation Repository

Repository: https://github.com/TRI-ML/packnet-sfm
Canonical: https://ross.abutalabs.com/products/packnet-sfm
Homepage: https://tri-ml.github.io/packnet-sfm/
Language: Python
License: MIT
License Family: permissive
Last push: 2023-07-16T22:02:53+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2463, "days_push": 1144, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1274, forks 246 (observed 2026-08-28T04:04:12.799051+00:00)

## What it is
Official PyTorch implementation of PackNet and related self-supervised monocular depth estimation methods from Toyota Research Institute's ML team. It provides training and evaluation code for depth and ego-motion estimation from monocular video, including support for non-pinhole cameras via Neural Ray Surfaces.

## Use cases
- estimate depth from monocular video
- train a self-supervised depth estimation model
- run monocular depth estimation on KITTI or DDAD
- estimate camera ego-motion from video
- deploy real-time depth estimation with TensorRT
- learn depth and pose on fisheye or catadioptric cameras
- reproduce CVPR 2020 PackNet paper results

## When to choose
- you need state-of-the-art self-supervised monocular depth estimation in PyTorch
- you want to reproduce or build on the PackNet or NRS papers
- you work with non-pinhole cameras like fisheye lenses
- you need a research baseline for depth and ego-motion learning

## When to avoid
- you need active development or support for new publications - use TRI-ML/vidar instead
- you lack an NVIDIA GPU with at least 6GB of memory
- you need a production-ready depth estimation service rather than research code
- you need stereo or LiDAR-based depth estimation

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, machine-learning, autonomous-vehicles, deep-learning
- platform: python
- tags: monocular-depth-estimation, self-supervised-learning, pytorch, depth-estimation, pose-estimation, research-code, packnet, kitti, ddad, linux, docker, gpu

## Member repositories
- TRI-ML/packnet-sfm (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:12.799051+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 2026-08-30T05:03:05.390215+00:00, confidence not recorded.
  - readme: https://github.com/TRI-ML/packnet-sfm (fetched 2026-08-28T04:04:12.799051+00:00, sha 1e91a09cbf1f)
  - homepage: https://tri-ml.github.io/packnet-sfm/ (fetched 2026-08-29T12:14:22.036953+00:00, sha 98c5632429c5)
- Data as of 2026-08-30T08:39:29.467469+00:00.