Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
This work addresses a domain-specific problem for researchers and practitioners in agricultural robotics by providing a tool to create synthetic training data, though it is incremental as it builds on existing procedural rendering and Domain Randomization techniques.
The authors tackled the scarcity of labeled training datasets for image analysis in robotic fruit harvesting by developing a procedural fruit tree rendering framework that generates synthetic datasets with ground truth semantic segmentation, incorporating Domain Randomization for variability.
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.