Finding corresponding pixels within a pair of images is a fundamental
computer vision task with various applications. Due to the specific
requirements of different tasks like optical flow estimation and local feature
matching, previous works are primarily categorized into dense matching and
sparse feature matching focusing on specialized architectures along with
task-specific datasets, which may somewhat hinder the generalization
performance of specialized models. In this paper, we propose a deep model for
sparse and dense matching, termed RGM (Robust Generalist Matching). In
particular, we elaborately design a cascaded GRU module for refinement by
exploring the geometric similarity iteratively at multiple scales following an
additional uncertainty estimation module for sparsification. To narrow the gap
between synthetic training samples and real-world scenarios, we build a new,
large-scale dataset with sparse correspondence ground truth by generating
optical flow supervision with greater intervals. As such, we are able to mix up
various dense and sparse matching datasets, significantly improving the
training diversity. The generalization capacity of our proposed RGM is greatly
improved by learning the matching and uncertainty estimation in a two-stage
manner on the large, mixed data. Superior performance is achieved for zero-shot
matching and downstream geometry estimation across multiple datasets,
outperforming the previous methods by a large margin.

通过在多个尺度上迭代地探索几何相似性，并使用一种额外的不确定性估计模块进行稀疏化，我们提出了一种名为 RGM（Robust Generalist Matching）的深度模型，用于稀疏和密集匹配。通过生成具有较大间隔的光流监督，我们构建了一个包含稀疏对应关系真值的新的大规模数据集，从而缩小了合成训练样本和真实场景之间的差距。通过在大型混合数据上以两阶段方式学习匹配和不确定性估计，我们显著提高了我们所提出的 RGM 模型的泛化能力。在多个数据集上实现了零样本匹配和下游几何估计的卓越性能，优于先前的方法很多。