多视角协作匹配提升复杂场景下轨迹构建可靠性
CoMatcher: Multi-View Collaborative Feature Matching
- 通过多视角互补信息实现全局3D场景理解
- 利用跨视角投影一致性获得更可靠的匹配结果
- 适合大规模多视图匹配任务与复杂场景应用
本文提出一种多视角协作匹配策略，用于复杂场景下的可靠轨迹构建。现有基于成对匹配的方法在存在显著遮挡或极端视角变化时，常因独立配对的不确定性导致估计模糊。其根源在于仅依赖两视角观测难以准确还原复杂的三维结构，因3D到2D投影带来显著信息丢失。为此，我们提出CoMatcher，一种深度多视角匹配器，能够（i）利用不同视角的互补上下文线索形成整体3D场景理解，（ii）通过跨视角投影一致性推断出可靠的全局解。基于CoMatcher，我们进一步构建了群体化框架，充分挖掘跨视角关系以应对大规模匹配任务。在多种复杂场景下的大量实验表明，该方法显著优于主流的两视角匹配范式。
原文摘要 · Abstract (English)
This paper proposes a multi-view collaborative matching strategy for reliable track construction in complex scenarios. We observe that the pairwise matching paradigms applied to image set matching often result in ambiguous estimation when the selected independent pairs exhibit significant occlusions or extreme viewpoint changes. This challenge primarily stems from the inherent uncertainty in interpreting intricate 3D structures based on limited two-view observations, as the 3D-to-2D projection leads to significant information loss. To address this, we introduce CoMatcher, a deep multi-view matcher to (i) leverage complementary context cues from different views to form a holistic 3D scene understanding and (ii) utilize cross-view projection consistency to infer a reliable global solution. Building on CoMatcher, we develop a groupwise framework that fully exploits cross-view relationships for large-scale matching tasks. Extensive experiments on various complex scenarios demonstrate the superiority of our method over the mainstream two-view matching paradigm.
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