Toward a Procedural Fruit Tree Rendering Framework for Image Analysis

Thomas Duboudin (1) , Maxime Petit (1) , Liming Chen (1)

1 imagine - Extraction de Caractéristiques et Identification

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.

Mots clés: Procedural Model; Synthetic Fruit Dataset; Harvesting Robotics; Domain Randomization

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Modélisation et simulation; Robotique [cs.RO]

Thomas Duboudin, Maxime Petit, Liming Chen. Toward a Procedural Fruit Tree Rendering Framework for Image Analysis. 7th International Workshop on Image Analysis Methods in the Plant Sciences, Jul 2019, Lyon, France. pp.4 - 5. ⟨hal-02140367⟩