BlenderProc is a procedural Blender pipeline for photorealistic rendering that generates synthetic training images through Python scripting.
The tool addresses the challenge of creating large datasets of photorealistic synthetic images for computer vision tasks. It works by providing a Python API that runs inside Blender's Python environment, allowing users to procedurally construct 3D scenes, configure cameras, apply materials and lighting, and render multiple image types in a single pipeline execution. Users write scripts that load or construct scenes, set camera poses, and generate RGB, depth, normal, and segmentation outputs. The pipeline is designed to be run multiple times, each iteration producing a new scene with multiple rendered views.
BlenderProc suits projects requiring synthetic training data for vision tasks like pose estimation, 3D reconstruction, and object detection. It works well for researchers and practitioners who need to generate large annotated datasets without manual 3D modeling. The tool supports loading assets from multiple sources including standard 3D formats, ShapeNet, Haven, and 3D-FRONT datasets. It provides features for sampling object poses, applying physics simulation, configuring physically-based materials, and automatically lighting scenes. The pipeline outputs data in HDF5 containers with COCO and BOP annotation formats. A debugging mode allows visualization and iteration within the Blender GUI, and breakpoint debugging is supported in IDEs.
The project maintains active development with regular updates to core functionality. Documentation is comprehensive, including tutorials and examples covering the full range of capabilities. The codebase shows consistent refinement of the procedural pipeline architecture and expansion of supported asset sources and rendering options. Community engagement is reflected through published research papers describing the tool's design and applications.
