# Toward a Procedural Fruit Tree Rendering Framework for Image Analysis

Source: [Toward a Procedural Fruit Tree Rendering Framework for Image Analysis](<https://www.sota2.com/research/paper/68fa65fa1e209131cea0da6f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:40:30.056Z

Authors: Thomas Duboudin, Maxime Petit, Liming Chen

Published: 2019-07-10T14:45:14.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1907.04759>)

[PDF](<https://arxiv.org/pdf/1907.04759.pdf>)