迈向基于合成数据的海洋感知扩展
Towards Scaling Marine Perception with Synthetic Data
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文扩展OceanSim模拟器，提出合成数据生成流水线，用于训练水下感知模型，并在真实海胆检测任务上验证其有效性，探讨了模拟到真实泛化的关键因素。
AI中文摘要：
在具有挑战性的水下环境中，可扩展的机器学习受到缺乏标记的真实世界训练数据的严重限制。这些数据通常获取成本高昂且费力，使得大规模真实世界数据的收集和管理变得困难。然而，模拟数据可以帮助缩小这一差距，为水下感知的许多基于学习的任务提供支持。在这项工作中，我们扩展了OceanSim——一个基于IsaacSim的水下感知模拟器，增加了一个合成数据生成（SDG）流水线，用于训练在水下场景中使用的模型。所提出的流水线使用户能够生成大规模、自动标记、照片级逼真的数据集，并具有可配置的场景外观、结构和传感器设置。我们在一个真实世界的海胆检测任务上评估了该流水线，并研究了不同形式的合成场景变化如何影响模拟到真实的性能。基于这些实验，我们讨论了我们的结果发现、当前流水线的主要局限性，并确定了提高水下渲染保真度、场景多样性以及评估模拟到真实泛化能力的未来方向。开源代码可在以下网址找到：https://this https URL。
英文摘要：
Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is often expensive and laborious to gather, making large-scale real-world data challenging to gather and curate. However, simulated data can help close the gap, enabling many learning-based tasks for underwater perception. In this work, we extend OceanSim, an IsaacSim-based underwater perception simulator, with a Synthetic Data Generation (SDG) pipeline for training models to be used in underwater scenarios. The proposed pipeline enables users to generate large, automatically labeled, photorealistic datasets with configurable scene appearance, structure, and sensor settings. We evaluate the pipeline on a real-world sea urchin detection task and study how different forms of synthetic scene variation affect sim-to-real performance. Based on these experiments, we discuss findings on our results, main limitations of the current pipeline and identify future directions for improving underwater rendering fidelity, scene diversity, and the evaluation of sim-to-real generalization. The open-source code can be found at https://github.com/umfieldrobotics/OceanSim.
