用扫描和图像生成3D模型替代CAD，高效构建农业产品合成数据集。
A Comparative Study of 3D Model Acquisition Methods for Synthetic Data Generation of Agricultural Products
- 通过扫描或图像转3D获取高保真3D模型替代缺失的CAD文件。
- 合成数据训练后微调小规模真实数据，检测性能接近使用优质模型。
- 适合缺乏3D设计模型的农业视觉系统研发人员参考。
在制造业中，基于人工智能的计算机视觉系统广泛用于降低成本、提升效率。训练这些模型需要大量标注数据，而真实数据采集与标注成本高昂，尤其在高差异性、低产量的制造环境中更为显著。一种减少对真实数据依赖的常见方法是利用工业中普遍存在的计算机辅助设计（CAD）模型生成合成数据。然而，在农业领域这类模型难以获取，导致合成数据应用受限。本文提出多种替代CAD文件的3D模型获取技术，并评估其在生成农业产品合成数据集中的表现。实验以箱子拣选场景中区分石头与土豆的物体检测任务为例，验证了通过扫描或图像到3D的方法获得的高代表性3D模型可用于有效生成合成数据。进一步发现，仅用少量真实数据进行微调，即可显著提升模型性能，即使使用代表性较弱的模型也能达到相近效果。
原文摘要 · Abstract (English)
In the manufacturing industry, computer vision systems based on artificial intelligence (AI) are widely used to reduce costs and increase production. Training these AI models requires a large amount of training data that is costly to acquire and annotate, especially in high-variance, low-volume manufacturing environments. A popular approach to reduce the need for real data is the use of synthetic data that is generated by leveraging computer-aided design (CAD) models available in the industry. However, in the agricultural industry these models are not readily available, increasing the difficulty in leveraging synthetic data. In this paper, we present different techniques for substituting CAD files to create synthetic datasets. We measure their relative performance when used to train an AI object detection model to separate stones and potatoes in a bin picking environment. We demonstrate that using highly representative 3D models acquired by scanning or using image-to-3D approaches can be used to generate synthetic data for training object detection models. Finetuning on a small real dataset can significantly improve the performance of the models and even get similar performance when less representative models are used.
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