DLR-RM/BlenderProc

Health v2 · maintenance only

Adoption not part of the score

3684 stars · 518 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

BlenderProc is a procedural Python pipeline built on Blender for generating photorealistic synthetic training images with ground-truth annotations. It automates scene loading, object pose sampling, lighting, camera placement, and rendering of RGB, depth, normal, and segmentation outputs.

Use cases

• generate synthetic training images for object detection
• render depth and segmentation ground truth for pose estimation
• create photorealistic datasets from 3D models like ShapeNet or 3D-FRONT
• sample camera poses in a 3D scene for computer vision training
• produce COCO or BOP annotated synthetic data
• simulate physics and collisions in generated scenes

When to choose

• you need labeled synthetic data for training computer vision models
• you want reproducible, scriptable Blender-based rendering pipelines
• you need RGB, depth, normal, and segmentation outputs from the same scene
• you work with standard 3D asset datasets like BOP, ShapeNet, Haven, or 3D-FRONT

When to avoid

• you need real photographs rather than synthetic renders
• you want a GUI-driven 3D modeling workflow instead of scripted pipelines
• your project cannot accept GPL-3.0 licensing constraints
• you need lightweight rendering without a full Blender installation

Facets

library · maturity active

image-processing computer-vision machine-learning data-generation simulation graphics cli computer-vision machine-learning graphics simulation artificial-intelligence python cli blender synthetic-data photorealistic-rendering segmentation-masks depth-images camera-sampling 3d-scene-generation training-data linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DLR-RM/BlenderProc")