Toward a Procedural Fruit Tree Rendering Framework for Image Analysis

07/10/2019
by   Thomas Duboudin, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset