Dense geometric matching determines the dense pixel-wise correspondence between a source and support image corresponding to the same 3D structure. Prior works employ an encoder of transformer blocks to correlate the two-frame features. However, existing monocular pretraining tasks, e.g., image classification, and masked image modeling (MIM), can not pretrain the cross-frame module, yielding less optimal performance. To resolve this, we reformulate the MIM from reconstructing a single masked image to reconstructing a pair of masked images, enabling the pretraining of transformer module. Additionally, we incorporate a decoder into pretraining for improved upsampling results. Further, to be robust to the textureless area, we propose a novel cross-frame global matching module (CFGM). Since the most textureless area is planar surfaces, we propose a homography loss to further regularize its learning. Combined together, we achieve the State-of-The-Art (SoTA) performance on geometric matching. Codes and models are available at https://github.com/ShngJZ/PMatch.

翻译：密集几何匹配旨在确定同一三维结构对应的源图像与支持图像之间的密集像素级对应关系。现有工作采用Transformer编码器模块关联两帧特征，然而现有的单目预训练任务（如图像分类与掩码图像建模）无法对跨帧模块进行预训练，导致性能次优。为解决此问题，我们将掩码图像建模从重构单张掩码图像重构为重构一对掩码图像，从而实现对Transformer模块的预训练。此外，我们在预训练中引入解码器以改进上采样效果。针对无纹理区域鲁棒性问题，我们提出了一种新型跨帧全局匹配模块（CFGM）。由于大部分无纹理区域为平面表面，我们进一步引入单应性损失来约束其学习。综合上述方法，我们在几何匹配任务上取得了最先进的性能。代码与模型已开源至 https://github.com/ShngJZ/PMatch。