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Main Authors: Gavriel, Peter, Norton, Adam, Kimble, Kenneth, Zimmerman, Megan
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2411.06166
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author Gavriel, Peter
Norton, Adam
Kimble, Kenneth
Zimmerman, Megan
author_facet Gavriel, Peter
Norton, Adam
Kimble, Kenneth
Zimmerman, Megan
contents Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity of synthetic image training data we propose a framework for defining synthetic image data pipelines. Additionally we survey the literature to identify the most promising candidates for components of the proposed pipeline. We propose that defining such a pipeline will be beneficial in reducing development cycles and coordinating future research.
format Preprint
id arxiv_https___arxiv_org_abs_2411_06166
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Towards an Efficient Synthetic Image Data Pipeline for Training Vision-Based Robot Systems
Gavriel, Peter
Norton, Adam
Kimble, Kenneth
Zimmerman, Megan
Robotics
Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity of synthetic image training data we propose a framework for defining synthetic image data pipelines. Additionally we survey the literature to identify the most promising candidates for components of the proposed pipeline. We propose that defining such a pipeline will be beneficial in reducing development cycles and coordinating future research.
title Towards an Efficient Synthetic Image Data Pipeline for Training Vision-Based Robot Systems
topic Robotics
url https://arxiv.org/abs/2411.06166