Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization
Is This the Right Place? Geometric-Semantic Pose Verification for Indoor Visual Localization
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DOI：
10.1109/iccv.2019.00447
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Hajime Taira;Ignacio Rocco;J. Sedlár;M. Okutomi;Josef Sivic;T. Pajdla;Torsten Sattler;A. Torii
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文献类型：
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作者：
Hajime Taira;Ignacio Rocco;J. Sedlár;M. Okutomi;Josef Sivic;T. Pajdla;Torsten Sattler;A. Torii
在由弱纹理房间和重复几何图案主导的大型复杂室内场景中，视觉定位是一个具有挑战性的问题，对于增强现实和机器人等应用具有高度的实际意义。为了处理这种情况下出现的歧义，一个常见的策略是，首先，生成多个估计的相机姿态，从给定的查询图像。与查询图像具有最大几何一致性的姿态，例如，然后在第二阶段中选择内围值计数的形式。虽然大量的研究集中在第一阶段，但在第二阶段的工作却少得多。因此，在本文中，我们专注于姿势验证。我们表明，结合不同的方式，即外观，几何和语义，大大提高了姿态验证，从而构成准确性。我们开发了多种手工制作的方法以及可训练的方法来加入几何语义验证，并在一个非常具有挑战性的室内数据集上显示出对最先进技术的显着改进。
Visual localization in large and complex indoor scenes, dominated by weakly textured rooms and repeating geometric patterns, is a challenging problem with high practical relevance for applications such as Augmented Reality and robotics. To handle the ambiguities arising in this scenario, a common strategy is, first, to generate multiple estimates for the camera pose from which a given query image was taken. The pose with the largest geometric consistency with the query image, e.g., in the form of an inlier count, is then selected in a second stage. While a significant amount of research has concentrated on the first stage, there has been considerably less work on the second stage. In this paper, we thus focus on pose verification. We show that combining different modalities, namely appearance, geometry, and semantics, considerably boosts pose verification and consequently pose accuracy. We develop multiple hand-crafted as well as a trainable approach to join into the geometric-semantic verification and show significant improvements over state-of-the-art on a very challenging indoor dataset.