# NVlabs/Deep_Object_Pose

Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)

Repository: https://github.com/NVlabs/Deep_Object_Pose
Canonical: https://ross.abutalabs.com/products/deep_object_pose
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-08-06T18:35:10+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 35, release rhythm 35, longevity 100
- inputs: {"age_days": 2882, "days_push": 392, "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 1178, forks 304 (observed 2026-08-28T04:03:52.868969+00:00)

## What it is
NVIDIA's Deep Object Pose Estimation (DOPE), a deep learning system for detecting known objects and estimating their 6-DoF pose from RGB camera images. It includes training, inference, evaluation, and synthetic data generation code, plus ROS1 and ROS2 (via Isaac ROS) packages for robotics integration.

## Use cases
- estimate 6-DoF pose of known objects from RGB images
- train a pose estimation model on custom objects
- generate synthetic training data for object pose estimation
- run object pose estimation in a ROS robotics pipeline
- evaluate pose estimation results on YCB or HOPE datasets
- detect and localize objects for robot manipulation

## When to choose
- you need 6-DoF pose estimation of known objects from RGB cameras
- you are building a robotics application with ROS1 or ROS2
- you want to train pose estimation models on your own objects with synthetic data
- you work with YCB or HOPE benchmark datasets

## When to avoid
- you need pose estimation for unknown or novel objects without training
- you require a permissively licensed model for commercial use (CC BY-NC-SA 4.0 is non-commercial)
- you need RGB-D-only or depth-based methods rather than RGB
- you lack an NVIDIA GPU for inference

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, machine-learning, deep-learning, sdk
- domain: robotics, computer-vision, artificial-intelligence
- platform: python
- tags: 6dof-pose-estimation, ros, object-detection, ycb, hope-dataset, nvidia, corl-2018, linux, gpu

## Member repositories
- NVlabs/Deep_Object_Pose (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:52.868969+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 2026-08-30T06:26:05.331481+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/Deep_Object_Pose (fetched 2026-08-28T04:03:52.868969+00:00, sha 384315ec6e4c)
- Data as of 2026-08-30T08:39:29.467469+00:00.