LeCoT：重新审视用于双视图对应点修剪的网络架构
LeCoT: revisiting network architecture for two-view correspondence pruning
AI总结：
本文提出双视图对应点修剪网络LeCoT，通过空间-通道融合Transformer模块和渐进式预测模块高效利用全局上下文信息，在多项几何任务中超越现有方法。
摘要：
双视图对应点修剪旨在从初始对应点中准确去除错误对应点（外点），并广泛应用于各种计算机视觉任务。当前流行的策略采用多层感知机（MLP）作为骨干网络，并辅以额外模块来增强网络处理上下文信息的能力，而处理上下文信息是MLP的一个已知局限。相比之下，我们引入了一种无需额外设计模块即可捕获对应点上下文信息的新视角。为此，我们设计了一个名为LeCoT的双视图对应点修剪网络，它能够在不同阶段自然地利用全局上下文信息。具体而言，LeCoT的核心设计是空间-通道融合Transformer模块，这是一个新提出的组件，能够高效地利用稀疏对应点之间的空间和通道全局上下文信息。此外，我们集成了所提出的预测模块，该模块利用来自中间阶段的对应点特征生成概率集，作为后续学习阶段的引导信息，使网络能够更有效地捕获鲁棒的全局上下文信息。值得注意的是，该预测模块逐步细化概率集，从而缓解了传统预测模块中常见的信息丢失问题。大量实验证明，所提出的LeCoT在对应点修剪、相对位姿估计、单应性估计、视觉定位和3D重建任务中优于最先进的方法。代码提供于https://github.com/Dailuanyuan2024/LeCoT-Revisiting-Network-Architecture-for-Two-View-Correspondence-Pruning。
英文摘要：
Two-view correspondence pruning aims to accurately remove incorrect correspondences (outliers) from initial ones and is widely applied to various computer vision tasks. Current popular strategies adopt multilayer perceptron (MLP) as the backbone, supplemented by additional modules to enhance the network ability to handle context information, which is a known limitation of MLPs. In contrast, we introduce a novel perspective for capturing correspondence context information without extra design modules. To this end, we design a two-view correspondence pruning network called LeCoT, which can naturally leverage global context information at different stages. Specifically, the core design of LeCoT is the Spatial-Channel Fusion Transformer block, a newly proposed component that efficiently utilizes both spatial and channel global context information among sparse correspondences. In addition, we integrate the proposed prediction block that utilizes correspondence features from intermediate stages to generate a probability set, which acts as guiding information for subsequent learning phases, allowing the network to more effectively capture robust global context information. Notably, this prediction block progressively refines the probability set, thereby mitigating the issue of information loss that is common in the traditional one. Extensive experiments prove that the proposed LeCoT outperforms state-of-the-art methods in correspondence pruning, relative pose estimation, homography estimation, visual localization, and $3$D~reconstruction tasks. The code is provided in https://github.com/Dailuanyuan2024/LeCoT-Revisiting-Network-Architecture-for-Two-View-Correspondence-Pruning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
