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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
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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.
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Submitted on : Friday, June 7, 2019 - 12:50:52 PM
Last modification on : Friday, September 30, 2022 - 11:34:16 AM


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  • HAL Id : hal-02140367, version 1
  • ARXIV : 1907.04759


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⟩



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