合成封闭回声：一种缩小仿真与真实声纳数据差距的新数据集
Synthetic Enclosed Echoes: A New Dataset to Mitigate the Gap Between Simulated and Real-World Sonar Data
AI总结：
本文提出合成封闭回声（SEE）数据集，结合高保真合成与真实声纳数据，以提升水下三维重建，并改进最先进算法，验证了其现实应用可行性。
摘要：
本文介绍了合成封闭回声（SEE），一种旨在增强水下环境中机器人感知和三维重建能力的新型数据集。SEE包含高保真合成声纳数据，并辅以少量真实世界声纳数据子集。为便于灵活获取数据，开发了一个模拟环境，能够通过添加新结构或成像声纳配置等修改生成额外数据。这种混合方法利用了合成数据的优势，包括易于获取的基准真值和生成多样化数据集的能力，同时通过在同一环境中获取的真实世界数据弥合仿真与现实的差距。SEE数据集全面评估了基于声学数据的方法，包括基于数学的声纳方法和深度学习算法。这些技术被用于验证数据集，确认其适用于水下三维重建。此外，本文提出了一种对最先进算法的新颖改进，展示了相比现有方法的性能提升。SEE数据集使得在现实场景中评估基于声学数据的方法成为可能，从而提高了它们在实际水下应用中的可行性。
英文摘要：
This paper introduces Synthetic Enclosed Echoes (SEE), a novel dataset designed to enhance robot perception and 3D reconstruction capabilities in underwater environments. SEE comprises high-fidelity synthetic sonar data, complemented by a smaller subset of real-world sonar data. To facilitate flexible data acquisition, a simulated environment has been developed, enabling the generation of additional data through modifications such as the inclusion of new structures or imaging sonar configurations. This hybrid approach leverages the advantages of synthetic data, including readily available ground truth and the ability to generate diverse datasets, while bridging the simulation-to-reality gap with real-world data acquired in a similar environment. The SEE dataset comprehensively evaluates acoustic data-based methods, including mathematics-based sonar approaches and deep learning algorithms. These techniques were employed to validate the dataset, confirming its suitability for underwater 3D reconstruction. Furthermore, this paper proposes a novel modification to a state-of-the-art algorithm, demonstrating improved performance compared to existing methods. The SEE dataset enables the evaluation of acoustic data-based methods in realistic scenarios, thereby improving their feasibility for real-world underwater applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
