构建系统化图像匹配基准，评估不同几何挑战下的方法表现。
RUBIK: A Structured Benchmark for Image Matching across Geometric Challenges
- 基于重叠度、缩放比和视角角设计33级难度标准。
- 最先进方法成功率达54.8%，仍难应对极端几何变化。
- 适合研究图像匹配鲁棒性与效率的开发者参考。
相机位姿估计在众多计算机视觉应用中至关重要，但现有基准难以揭示方法在不同几何挑战下的局限性。我们提出RUBIK，一个新型基准，系统评估图像匹配方法在明确几何难度等级下的表现。基于重叠度、缩放比和视角角三个互补指标，我们将nuScenes数据集中的16.5K张图像对划分为33个难度级别。对14种方法的全面评估显示，尽管无检测器方法表现最优（成功率>47%），但其计算开销显著高于基于检测器的方法（150-600ms vs. 40-70ms）。即使表现最佳的方法也仅在54.8%的图像对上成功，表明在低重叠、大尺度差异与极端视角变化共现的挑战场景中仍有巨大改进空间。该基准将公开发布。
原文摘要 · Abstract (English)
Camera pose estimation is crucial for many computer vision applications, yet existing benchmarks offer limited insight into method limitations across different geometric challenges. We introduce RUBIK, a novel benchmark that systematically evaluates image matching methods across well-defined geometric difficulty levels. Using three complementary criteria - overlap, scale ratio, and viewpoint angle - we organize 16.5K image pairs from nuScenes into 33 difficulty levels. Our comprehensive evaluation of 14 methods reveals that while recent detector-free approaches achieve the best performance (>47% success rate), they come with significant computational overhead compared to detector-based methods (150-600ms vs. 40-70ms). Even the best performing method succeeds on only 54.8% of the pairs, highlighting substantial room for improvement, particularly in challenging scenarios combining low overlap, large scale differences, and extreme viewpoint changes. Benchmark will be made publicly available.
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