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
this https URL

本文通过对预训练模块重构和加入新模块，实现了稠密几何匹配任务的最优表现，并提供了相应的代码和模型。

PMatch：用于密集几何匹配的成对掩蔽图像建模

PMatch: Paired Masked Image Modeling for Dense Geometric Matching

Monocular 3D object detection is an essential task in autonomous driving.
However, most current methods consider each 3D object in the scene as an
independent training sample, while ignoring their inherent geometric relations,
thus inevitably resulting in a lack of leveraging spatial constraints. In this
paper, we propose a novel method that takes all the objects into consideration
and explores their mutual relationships to help better estimate the 3D boxes.
Moreover, since 2D detection is more reliable currently, we also investigate
how to use the detected 2D boxes as guidance to globally constrain the
optimization of the corresponding predicted 3D boxes. To this end, a
differentiable loss function, termed as Homography Loss, is proposed to achieve
the goal, which exploits both 2D and 3D information, aiming at balancing the
positional relationships between different objects by global constraints, so as
to obtain more accurately predicted 3D boxes. Thanks to the concise design, our
loss function is universal and can be plugged into any mature monocular 3D
detector, while significantly boosting the performance over their baseline.
Experiments demonstrate that our method yields the best performance (Nov. 2021)
compared with the other state-of-the-arts by a large margin on KITTI 3D
datasets.

本文提出了一种新的单目三维目标检测方法，通过考虑物体间的几何关系，探索它们的相互关系来更好地估计三维框，并使用检测到的二维框来全局约束相应的三维框的优化，通过提出的可微分损失函数（Homography Loss）来实现这一目标。实验证明，该方法表现出比其他最先进的方法在 KITTI 3D 数据集上具有更好的性能。