  We propose a procedural fruit tree rendering framework, based on Blender and
Python scripts allowing to generate quickly labeled dataset (i.e. including
ground truth semantic segmentation). It is designed to train image analysis
deep learning methods (e.g. in a robotic fruit harvesting context), where real
labeled training datasets are usually scarce and existing synthetic ones are
too specialized. Moreover, the framework includes the possibility to introduce
parametrized variations in the model (e.g. lightning conditions, background),
producing a dataset with embedded Domain Randomization aspect.
