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.
@article{arxiv.1907.04759,
title = {Toward a Procedural Fruit Tree Rendering Framework for Image Analysis},
author = {Thomas Duboudin and Maxime Petit and Liming Chen},
journal= {arXiv preprint arXiv:1907.04759},
year = {2019}
}