发表机构
College of Engineering at the University of Florida; Seoul National University; University of South Florida; University of Miami（佛罗里达大学工程学院; 首尔大学; 南佛罗里达大学; 迈阿密大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对声呐噪声导致水下目标识别困难的问题，采用前视声呐与三维声呐两种模态，通过自动校准过滤噪声特征，提升了特征提取性能，为水下目标识别提供了有效方案。
AI 中文摘要
声呐会产生大量噪声。随着能够生成完整三维点云的新技术出现，稀疏点云中的噪声被放大，使得识别用于导航、识别或重建的特征变得极具挑战性。为应对这一挑战，我们提出采用两种不同的声呐模态：一种生成二维强度图像，另一种生成三维点云。通过实现自动校准，我们可以过滤掉不同模态间的噪声特征，以增强特征提取。实验表明，自动校准相较于手动校准提升了5%的性能，且与原始点云相比，过滤操作使特征提取提升了40%以上。代码和数据集可在该https网址获取。
英文摘要
Sonars generate a significant amount of noise. With the advent of new technology capable of producing full 3D point clouds, the noise is amplified in sparse point clouds, making it challenging to recognize features for navigation, recognition, or reconstruction. To address this challenge, we propose using two different sonar modalities: one that produces a 2D intensity image and another that generates a 3D point cloud. By implementing auto-calibration, we can filter out noisy features between the modalities to enhance feature extraction. Experiments demonstrate that auto-calibration improves performance over manual calibration by 5% and that filtering enhances feature extraction by more than 40% relative to the raw point cloud. Code and datasets are given at https://theaprilab.org/fls-3d-calibrator
Comments6 pages, Accepted to IEEE OCEANS 2026
