论文标题
Adalam：重新访问手工制作的离群值检测
AdaLAM: Revisiting Handcrafted Outlier Detection
论文作者
论文摘要
局部功能匹配是许多计算机视觉管道的关键组成部分，包括从结构上移动，大满贯和视觉定位。但是，由于描述符的局限性，原始匹配通常会受到大多数异常值的污染。结果，离群值检测是计算机视觉中的一个基本问题，并且在过去几十年中提出了广泛的方法。在本文中，我们重新访问手工制作的方法以进行离群过滤。基于最佳实践，我们提出了一个分层管道，以进行有效的离群检测，并整合了新颖的思想，从而导致阿达拉姆（Adalam）是一种有效而有竞争力的方法，以实现拒绝。 Adalam旨在有效利用现代并行硬件，从而产生非常快速但非常准确的异常过滤器。我们在多个大型且多样化的数据集中验证了Adalam，并提交图像匹配挑战（CVPR2020），并通过简单的基线描述符获得竞争结果。我们表明，在效率和有效性方面，阿达拉姆（Adalam）与当前最新水平的竞争力更大。
Local feature matching is a critical component of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision, and a wide range of approaches have been proposed over the last decades. In this paper we revisit handcrafted approaches to outlier filtering. Based on best practices, we propose a hierarchical pipeline for effective outlier detection as well as integrate novel ideas which in sum lead to AdaLAM, an efficient and competitive approach to outlier rejection. AdaLAM is designed to effectively exploit modern parallel hardware, resulting in a very fast, yet very accurate, outlier filter. We validate AdaLAM on multiple large and diverse datasets, and we submit to the Image Matching Challenge (CVPR2020), obtaining competitive results with simple baseline descriptors. We show that AdaLAM is more than competitive to current state of the art, both in terms of efficiency and effectiveness.
