DIYer22/bpycv
Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)
This project helps computer vision researchers and robotics engineers efficiently generate synthetic image datasets. It takes 3D models and scene descriptions from Blender and produces rendered images with corresponding ground truth annotations like instance segmentation masks, depth maps, and 6D object poses. This streamlines the creation of diverse training data for tasks such as object detection, segmentation, and pose estimation.
498 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to create large, varied datasets of synthetic images with detailed ground truth for training computer vision models, especially for robotics or industrial applications.
Not ideal if you are working exclusively with real-world image data or do not have access to 3D object models and Blender for scene setup.
Stars
498
Forks
59
Language
Python
License
MIT
Category
Last pushed
Aug 05, 2025
Commits (30d)
0
Dependencies
5
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/computer-vision/DIYer22/bpycv"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
DeepLabCut/DeepLabCut
Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with...
openpifpaf/openpifpaf
Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and...
lambdaloop/anipose
🐜🐀🐒🚶 A toolkit for robust markerless 3D pose estimation
NeLy-EPFL/DeepFly3D
Motion capture (markerless 3D pose estimation) pipeline and helper GUI for tethered Drosophila.
NVIDIA-ISAAC-ROS/isaac_ros_pose_estimation
Deep learned, NVIDIA-accelerated 3D object pose estimation