BlenderProc
This provides a tool for researchers and practitioners needing synthetic training data, though it is incremental as it builds on existing Blender capabilities.
The authors tackled the problem of generating realistic training images for convolutional neural networks by developing BlenderProc, a modular procedural pipeline extension for Blender that produces images usable for tasks like segmentation and pose estimation.
BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks. These can be used in a variety of use cases including segmentation, depth, normal and pose estimation and many others. A key feature of our extension of blender is the simple to use modular pipeline, which was designed to be easily extendable. By offering standard modules, which cover a variety of scenarios, we provide a starting point on which new modules can be created.