学会解决最简单的难题

IF 20.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2023-08-23 DOI:10.1109/TPAMI.2023.3307898
Petr Hruby, Timothy Duff, Anton Leykin, Tomas Pajdla
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引用次数: 0

摘要

我们提出了一种在 RANSAC 框架下解决困难几何优化问题的方法。硬最小问题产生于将原始几何优化问题放宽为具有许多虚假解的最小问题。我们的方法可以避免计算大量虚假解。我们设计了一种学习策略,用于选择起始问题-解决方案对,该问题和感兴趣的解决方案可以在数值上继续。我们通过使用每个视图中的四个点进行最小松弛,为计算三个校准摄像机的相对姿态问题开发了一个 RANSAC 求解器,从而演示了我们的方法。平均而言,我们可以在 70 μs 内解决一个问题。我们还对我们的工程选择进行了基准测试和研究,该选择适用于我们非常熟悉的问题,即通过两个视图中五个点的最小情况计算两个校准摄像机的相对姿态。
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Learning to Solve Hard Minimal Problems.

We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 μs. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two views.

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来源期刊
CiteScore
28.40
自引率
3.00%
发文量
885
审稿时长
8.5 months
期刊介绍: The IEEE Transactions on Pattern Analysis and Machine Intelligence publishes articles on all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence, with a particular emphasis on machine learning for pattern analysis. Areas such as techniques for visual search, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.
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