This paper presents HITNet, a novel neural network architecture for real-time
stereo matching. Contrary to many recent neural network approaches that operate
on a full cost volume and rely on 3D convolutions, our approach does not
explicitly build a volume and instead relies on a fast multi-resolution
initialization step, differentiable 2D geometric propagation and warping
mechanisms to infer disparity hypotheses. To achieve a high level of accuracy,
our network not only geometrically reasons about disparities but also infers
slanted plane hypotheses allowing to more accurately perform geometric warping
and upsampling operations. Our architecture is inherently multi-resolution
allowing the propagation of information across different levels. Multiple
experiments prove the effectiveness of the proposed approach at a fraction of
the computation required by state-of-the-art methods. At the time of writing,
HITNet ranks 1st-3rd on all the metrics published on the ETH3D website for two
view stereo, ranks 1st on most of the metrics among all the end-to-end learning
approaches on Middlebury-v3, ranks 1st on the popular KITTI 2012 and 2015
benchmarks among the published methods faster than 100ms.

本文提出了 HITNet，一种新型神经网络结构，用于实时立体匹配。该算法使用多分辨率初始化步骤、可微 2D 几何传播和变形机制推断差异假设，是一种具有高灵敏度的架构，可在多个分类指标上获得最好的表现。

HITNet：用于实时立体匹配的分层迭代瓷砖细化网络

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time  Stereo Matching

Neural networks designed for the task of classification have become a
commodity in recent years. Many works target the development of more effective
networks, which results in a complexification of their architectures with more
layers, multiple sub-networks, or even the combination of multiple classifiers,
but this often comes at the expense of producing uninterpretable black boxes.
In this paper, we redesign a simple capsule network to enable it to synthesize
class-representative samples, called prototypes, by replacing the last layer
with a novel Hit-or-Miss layer. This layer contains activated vectors, called
capsules, that we train to hit or miss a fixed target capsule by tailoring a
specific centripetal loss function. This possibility allows to develop a data
augmentation step combining information from the data space and the feature
space, resulting in a hybrid data augmentation process. We show that our
network, named HitNet, is able to reach better performances than those
reproduced with the initial CapsNet on several datasets, while allowing to
visualize the nature of the features extracted as deformations of the
prototypes, which provides a direct insight into the feature representation
learned by the network .

本文介绍一种名为 HitNet 的神经网络，它使用胶囊网络结构，通过添加一层新型的 Hit-or-Miss 层来合成代表类别的样本，该层包含被激活的向量标称为胶囊。通过针对中心损失函数的定制化以训练胶囊，我们能够组合来自数据空间和特征空间的信息，从而实现混合数据增强过程，HitNet 能够比初始 CapsNet 在多个数据集上都获得更好的性能，并且还能够可视化该网络学习到的特征表示的本质，从而提供对特征表示学习的直接洞察。