果樹画像解析のための手続き的レンダリングフレームワーク
Toward a Procedural Fruit Tree Rendering Framework for Image Analysis
BlenderとPythonで果樹の合成画像と正解セグメンテーションを自動生成し、照明や背景のランダム化でロボット収穫向け深層学習の学習データを効率的に作る手法を提案。
著者: Thomas Duboudin, Maxime Petit, Liming Chen
分類: cs.CV, cs.RO
原文アブストラクト
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.
