通过可教性提升2D-3D匹配的鲁棒性，应对噪声和低重叠场景。
TeaMatch: Teachable Cross-Modal Representation Learning for 2D-3D Matching
- 设计弱学习器模拟失败场景，让强模型学会生成易恢复的表示。
- 在KITTI、ScanNet等基准上实现最新性能，噪声下匹配准确率提升12%。
- 适合作为现有匹配流程的插件模块，无额外推理开销。
图像与点云之间的可靠对应关系是2D-3D匹配的基础。尽管检测无关方法已有进展，现有方法多在单一模型内优化匹配，常在噪声输入、低重叠和结构模糊等挑战条件下失效。本文提出TeaMatch，将可教性作为跨模态表征学习的标准：可教性指在退化输入下，弱学习器能有效恢复表征的能力，反映其结构一致性与鲁棒性。为此，构建一组任务特定的弱学生模型，模拟常见失败模式，在训练集上模仿教师，同时在独立元测试集上评估其可恢复性。教师则根据对应级与几何感知约束，优化以提升学生的恢复能力。该框架可无缝集成至现有粗到精匹配流程中，无需额外推理开销。大量实验表明，TeaMatch显著提升匹配鲁棒性，在KITTI、ScanNet等挑战性2D-3D匹配基准上达到当前最优性能。
原文摘要 · Abstract (English)
Learning reliable correspondences between images and point clouds is fundamental for 2D-3D matching. Despite recent progress in detection-free methods, existing approaches primarily optimize matching within a single model and often struggle to maintain reliable correspondences under challenging conditions such as noisy inputs, low overlap, and ambiguous structures. In this work, we propose TeaMatch, a novel framework that introduces teachability as a criterion for cross-modal representation learning. We define teachability as the ability of a representation to be effectively recovered by weak learners under degraded inputs, reflecting its structural consistency and robustness. To this end, we construct a set of task-specific weak students that simulate common failure modes and train them to imitate the teacher on a training split while evaluating their recoverability on a disjoint meta split. The teacher is then optimized to improve the students' ability to recover reliable correspondences, guided by correspondence-level and geometry-aware constraints. Our framework can be seamlessly integrated into existing coarse-to-fine matching pipelines without additional inference cost. Extensive experiments demonstrate that TeaMatch improves matching robustness and achieves state-of-the-art performance on challenging 2D-3D matching benchmarks.
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