Robust model fitting is a core algorithm in a large number of computer vision
applications. Solving this problem efficiently for datasets highly contaminated
with outliers is, however, still challenging due to the underlying
computational complexity. Recent literature has focused on learning-based
algorithms. However, most approaches are supervised which require a large
amount of labelled training data. In this paper, we introduce a novel
unsupervised learning framework that learns to directly solve robust model
fitting. Unlike other methods, our work is agnostic to the underlying input
features, and can be easily generalized to a wide variety of LP-type problems
with quasi-convex residuals. We empirically show that our method outperforms
existing unsupervised learning approaches, and achieves competitive results
compared to traditional methods on several important computer vision problems.

本文提出了一种新颖的无监督学习框架，能够直接解决鲁棒模型拟合问题，相比之前的无监督学习方法，本方法在多个重要的计算机视觉问题上表现出色，并与传统方法取得了有竞争力的结果。

基于强化学习的无监督学习对鲁棒拟合的应用

Unsupervised Learning for Robust Fitting:A Reinforcement Learning  Approach

We present self-supervised geometric perception (SGP), the first general
framework to learn a feature descriptor for correspondence matching without any
ground-truth geometric model labels (e.g., camera poses, rigid
transformations). Our first contribution is to formulate geometric perception
as an optimization problem that jointly optimizes the feature descriptor and
the geometric models given a large corpus of visual measurements (e.g., images,
point clouds). Under this optimization formulation, we show that two important
streams of research in vision, namely robust model fitting and deep feature
learning, correspond to optimizing one block of the unknown variables while
fixing the other block. This analysis naturally leads to our second
contribution -- the SGP algorithm that performs alternating minimization to
solve the joint optimization. SGP iteratively executes two meta-algorithms: a
teacher that performs robust model fitting given learned features to generate
geometric pseudo-labels, and a student that performs deep feature learning
under noisy supervision of the pseudo-labels. As a third contribution, we apply
SGP to two perception problems on large-scale real datasets, namely relative
camera pose estimation on MegaDepth and point cloud registration on 3DMatch. We
demonstrate that SGP achieves state-of-the-art performance that is on-par or
superior to the supervised oracles trained using ground-truth labels.

自监督几何感知（SGP）框架通过联合优化特征描述和几何模型，解决了对应关系匹配的问题，在相机姿态和刚性转换等缺少地面真值标签的情况下展示了其在实际数据集上令人瞩目的优越性能。

自监督几何感知

Self-supervised Geometric Perception

Robust model fitting plays a vital role in computer vision, and research into
algorithms for robust fitting continues to be active. Arguably the most popular
paradigm for robust fitting in computer vision is consensus maximisation, which
strives to find the model parameters that maximise the number of inliers.
Despite the significant developments in algorithms for consensus maximisation,
there has been a lack of fundamental analysis of the problem in the computer
vision literature. In particular, whether consensus maximisation is "tractable"
remains a question that has not been rigorously dealt with, thus making it
difficult to assess and compare the performance of proposed algorithms,
relative to what is theoretically achievable. To shed light on these issues, we
present several computational hardness results for consensus maximisation. Our
results underline the fundamental intractability of the problem, and resolve
several ambiguities existing in the literature.

本研究探讨了计算机视觉中强健模型拟合算法的一个主要范式：共识最大化，并就其可解性进行了理论分析，结果显示该问题基本上是不可解的。