Correspondences emerge from large-scale vision models trained for generative
and discriminative tasks. This has been revealed and benchmarked by computing
correspondence maps between pairs of images, using nearest neighbors on the
feature grids. Existing work has attempted to improve the quality of these
correspondence maps by carefully mixing features from different sources, such
as by combining the features of different layers or networks. We point out that
a better correspondence strategy is available, which directly imposes structure
on the correspondence field: the functional map. Wielding this simple
mathematical tool, we lift the correspondence problem from the pixel space to
the function space and directly optimize for mappings that are globally
coherent. We demonstrate that our technique yields correspondences that are not
only smoother but also more accurate, with the possibility of better reflecting
the knowledge embedded in the large-scale vision models that we are studying.
Our approach sets a new state-of-the-art on various dense correspondence tasks.
We also demonstrate our effectiveness in keypoint correspondence and affordance
map transfer.

使用功能映射方法来提高大规模视觉模型训练中的对应关系地图的质量，并在各种密集对应任务中取得了最新的最佳性能。

零样本图像特征一致性与深度函数图

Zero-Shot Image Feature Consensus with Deep Functional Maps

Understanding the geometry and pose of objects in 2D images is a fundamental
necessity for a wide range of real world applications. Driven by deep neural
networks, recent methods have brought significant improvements to object pose
estimation. However, they suffer due to scarcity of keypoint/pose-annotated
real images and hence can not exploit the object's 3D structural information
effectively. In this work, we propose a data-efficient method which utilizes
the geometric regularity of intraclass objects for pose estimation. First, we
learn pose-invariant local descriptors of object parts from simple 2D RGB
images. These descriptors, along with keypoints obtained from renders of a
fixed 3D template model are then used to generate keypoint correspondence maps
for a given monocular real image. Finally, a pose estimation network predicts
3D pose of the object using these correspondence maps. This pipeline is further
extended to a multi-view approach, which assimilates keypoint information from
correspondence sets generated from multiple views of the 3D template model.
Fusion of multi-view information significantly improves geometric comprehension
of the system which in turn enhances the pose estimation performance.
Furthermore, use of correspondence framework responsible for the learning of
pose invariant keypoint descriptor also allows us to effectively alleviate the
data-scarcity problem. This enables our method to achieve state-of-the-art
performance on multiple real-image viewpoint estimation datasets, such as
Pascal3D+ and ObjectNet3D. To encourage reproducible research, we have released
the codes for our proposed approach.

利用物体分类内部几何规律的数据驱动姿态估计方法。首先，从 2D RGB 图像学习物体部分的姿态不变局部描述符。将这些描述符与来自固定３D 模板的关键点结合使用，生成给定单眼实际图像的关键点对应图。最后，使用这些关键点对应图，预测物体的３D 姿态。该方法在多个真实环境的视角估计数据集，如 Pascal3D + 与 ObjectNet3D 上的表现达到最佳水平，并且我们公开了所使用方法的代码以鼓励再生研究。