Skip to content

Unity-Technologies/Robotics-Object-Pose-Estimation

View on GitHub ↗Related repositories →

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.

active 2023-08-17 → 2026-04-09 (UTC)

Complete coverage27,420 / 27,420 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-09-30 (UTC)
Events
130
Pushes
0
Pull requests
0
Issues
0
Stars
108
Forks
18

Activity over time

Daily event counts in the loaded window

Line chart, 967 days from 2023-08-17 to 2026-04-09. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 4 total, peak 1 in a day. Stars: 108 total, peak 3 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
lunzard1001
xiaolijz1001
junofficial1001
WWWOWhite1001

Recent activity

Latest issues, pull requests and releases

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 108 stars here means stars gained during the window, not the repo's star count.