SC-Match：用于侧扫声纳测绘的具有上下文一致性的尺度空间匹配
SC-Match: Scale-Space Matching with Context Consistency for Side-Scan Sonar Mapping
浏览论文内容
中文总结 AI 辅助
研究针对侧扫声纳测绘中对应关系估计难题，提出SC-Match框架，通过尺度空间表示和上下文一致细化提高可靠性，无需特定声纳训练，实验表明其在不同条件下比预训练方法更优，能提供更准确对应和几何对齐。
中文摘要 AI 辅助
重叠侧扫声纳（SSS）测量之间空间对应关系的可靠估计对测绘至关重要，但声学外观变化、海底纹理弱、重复模式和阴影使这些对应关系稀疏、不稳定且依赖上下文。稀缺的点级注释进一步限制了针对声纳的深度匹配模型训练或微调。为此，我们提出SC-Match，这是一个无需训练的尺度空间匹配框架，通过上下文一致的对应关系细化，使预训练的特征提取和匹配组件适应SSS观测，无需特定于声纳的再训练。该框架通过尺度空间表示和上下文一致的细化提高对应关系的可靠性。在特征表示方面，将冻结的提取器应用于多个观测尺度，根据输入SSS图像的结构-纹理趋势校准检测器响应，并使用分数感知跨尺度融合来保留紧凑的特征候选。在对应关系细化方面，使用相邻的局部匹配案例作为相邻上下文来验证稳定的固定-移动关系，并保留非冲突的互补匹配以进行对齐。在使用不同声纳平台在不同环境中获取的数据集上进行的实验表明，SC-Match比代表性的预训练方法提供更准确的对应关系和更一致的几何对齐，同时在未见的跨平台采集条件下保持稳定行为。
英文摘要
Reliable estimation of spatial correspondences between overlapping side-scan sonar (SSS) measurements is essential for mapping, but acoustic appearance variations, weak seabed texture, repetitive patterns, and shadows make such correspondences sparse, unstable, and context-dependent. Scarce point-level annotations further limit sonar-specific training or fine-tuning of deep matching models. To this end, we propose SC-Match, a training-free scale-space matching framework with context-consistent correspondence refinement that adapts pretrained feature extraction and matching components to SSS observations without sonar-specific retraining. The framework improves correspondence reliability through scale-space representation and context-consistent refinement. For feature representation, a frozen extractor is applied to multiple observation scales, detector responses are calibrated according to the structure--texture tendency of the input SSS image, and score-aware cross-scale fusion is used to retain compact feature candidates. For correspondence refinement, adjacent local matching cases are used as neighboring contexts to verify stable fixed--moving relations and preserve non-conflicting complementary matches for alignment. Experiments on datasets acquired in different environments using different sonar platforms show that SC-Match provides more accurate correspondences and more consistent geometric alignment than representative pretrained methods, while maintaining stable behavior under unseen cross-platform acquisition conditions.
