Establishing robust and accurate correspondences between a pair of images is
a long-standing computer vision problem with numerous applications. While
classically dominated by sparse methods, emerging dense approaches offer a
compelling alternative paradigm that avoids the keypoint detection step.
However, dense flow estimation is often inaccurate in the case of large
displacements, occlusions, or homogeneous regions. In order to apply dense
methods to real-world applications, such as pose estimation, image
manipulation, or 3D reconstruction, it is therefore crucial to estimate the
confidence of the predicted matches.
We propose the Enhanced Probabilistic Dense Correspondence Network, PDC-Net+,
capable of estimating accurate dense correspondences along with a reliable
confidence map. We develop a flexible probabilistic approach that jointly
learns the flow prediction and its uncertainty. In particular, we parametrize
the predictive distribution as a constrained mixture model, ensuring better
modelling of both accurate flow predictions and outliers. Moreover, we develop
an architecture and an enhanced training strategy tailored for robust and
generalizable uncertainty prediction in the context of self-supervised
training. Our approach obtains state-of-the-art results on multiple challenging
geometric matching and optical flow datasets. We further validate the
usefulness of our probabilistic confidence estimation for the tasks of pose
estimation, 3D reconstruction, image-based localization, and image retrieval.
Code and models are available at this https URL

提出了一种增强型概率密集对应网络 (PDC-Net+), 可以同时估计准确的密集对应关系和可靠的置信度分布，通过联合学习流估计和不确定性来预测密集匹配度，并且在自监督训练环境下具有更加稳健和可泛化的不确定性预测策略，对几何匹配和光流数据集取得了最先进的结果，并证明了概率置信度估计在姿态估计、3D 重建、图像定位和图像检索方面的有用性。

PDC-Net+：加强的概率密集对应网络

PDC-Net+: Enhanced Probabilistic Dense Correspondence Network

Establishing dense correspondences between a pair of images is an important
and general problem. However, dense flow estimation is often inaccurate in the
case of large displacements or homogeneous regions. For most applications and
down-stream tasks, such as pose estimation, image manipulation, or 3D
reconstruction, it is crucial to know when and where to trust the estimated
matches.
In this work, we aim to estimate a dense flow field relating two images,
coupled with a robust pixel-wise confidence map indicating the reliability and
accuracy of the prediction. We develop a flexible probabilistic approach that
jointly learns the flow prediction and its uncertainty. In particular, we
parametrize the predictive distribution as a constrained mixture model,
ensuring better modelling of both accurate flow predictions and outliers.
Moreover, we develop an architecture and training strategy tailored for robust
and generalizable uncertainty prediction in the context of self-supervised
training. Our approach obtains state-of-the-art results on multiple challenging
geometric matching and optical flow datasets. We further validate the
usefulness of our probabilistic confidence estimation for the task of pose
estimation. Code and models are available at
this https URL

提出了一种基于概率的方法，通过学习流场预测和不确定性，建立两幅图像间的密集对应关系，同时估计像素级置信度和可靠性，通过受限混合模型等方法建立更好的流预测和异常点建模，通过实验证明该方法在多个具有挑战性的几何匹配和光流估计数据集上具有最先进的效果，并在姿态估计领域证明了它的实用性。