用生成图像提升水下特征提取鲁棒性，让相机在浑浊水中也能精准定位。
Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images
- 通过自适应GAN生成逼真水下图像，模拟真实环境噪声与散射。
- 跨模态知识蒸馏将空中模型迁移到水下，提升特征匹配准确率。
- 适用于水下机器人视觉定位，尤其适合无GPS的深海探测场景。
自主水下航行器（AUV）在水下探索中至关重要。基于视觉的方法可在缺乏GPS、LiDAR等传统传感器时提供低成本的定位与建图方案。然而，水下环境因光衰减、散射及海洋雪干扰导致图像模糊和噪声严重，极大影响特征提取与匹配性能。本文提出一种基于生成对抗网络（GAN）合成图像的跨模态知识蒸馏方法，将空中训练的特征提取与匹配模型迁移至水下场景。首先设计一种自适应GAN合成方法，估计水体参数与噪声分布，生成具有环境特性的合成水下图像；随后构建通用知识蒸馏框架，兼容多种教师模型。实验表明，引入GAN生成的噪声与前向散射成分对模型性能提升显著。进一步在真实水下序列上部署视觉里程计（VSLAM），验证了迁移模型的有效性。项目主页：https://github.com/Jinghe-mel/UFEN-GAN。
原文摘要 · Abstract (English)
Autonomous Underwater Vehicles (AUVs) play a crucial role in underwater exploration. Vision-based methods offer cost-effective solutions for localization and mapping in the absence of conventional sensors like GPS and LiDAR. However, underwater environments present significant challenges for feature extraction and matching due to image blurring and noise caused by attenuation, scattering, and the interference of \textit{marine snow}. In this paper, we aim to improve the robustness of the feature extraction and matching in the turbid underwater environment using the cross-modal knowledge distillation method that transfers the in-air feature extraction and matching models to underwater settings using synthetic underwater images as the medium. We first propose a novel adaptive GAN-synthesis method to estimate water parameters and underwater noise distribution, to generate environment-specific synthetic underwater images. We then introduce a general knowledge distillation framework compatible with different teacher models. The evaluation of GAN-based synthesis highlights the significance of the new components, i.e. GAN-synthesized noise and forward scattering, in the proposed model. Additionally, VSLAM, as a representative downstream application of feature extraction and matching, is employed on real underwater sequences to validate the effectiveness of the transferred model. Project page: https://github.com/Jinghe-mel/UFEN-GAN.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。