论文标题
超越受控环境：3D摄像头重新定位在更改室内场景中
Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes
论文作者
论文摘要
长期的摄像头重新定位是一项重要的任务，具有众多的计算机视觉和机器人应用程序。尽管存在针对照明，天气和季节性变化的各种室外基准测试，但对在室内发生的外观变化的关注却少得多。这导致了流行的室内基准之间的不匹配，该基准的重点是静态场景和许多现实世界应用感兴趣的室内环境。在本文中，我们适应了3RSCAN - 最近引入的室内RGB-D数据集设计用于对象实例重新定位 - 创建RIO10，这是一种重点介绍室内场景的新的长期摄像机重新定位基准。我们提出了新的指标，以评估摄像头重新定位并探讨最先进的相机重新定位器根据这些指标的执行方式。我们还详细介绍了不同类型的场景变化如何基于在给定的RGB-D框架中检测此类变化的新方法，如何影响不同方法的性能。我们的结果清楚地表明，长期室内重新定位是一个未解决的问题。我们的基准和工具可在waldjohannau.github.io/rio10上公开获得
Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular indoor benchmarks, which focus on static scenes, and indoor environments that are of interest for many real-world applications. In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes. We propose new metrics for evaluating camera re-localization and explore how state-of-the-art camera re-localizers perform according to these metrics. We also examine in detail how different types of scene change affect the performance of different methods, based on novel ways of detecting such changes in a given RGB-D frame. Our results clearly show that long-term indoor re-localization is an unsolved problem. Our benchmark and tools are publicly available at waldjohannau.github.io/RIO10
