In this paper, we present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training. Our approach employs a novel matching procedure to localize the perspective camera's viewport, given as an RGB image, within a set of panoramic semantic layout representations of the indoor environment. The panoramas are rendered from an untextured 3D reference model, which only comprises approximate structural information about room shapes, along with door and window annotations. We demonstrate that a straightforward convolutional network structure can successfully achieve image-to-panorama and ultimately image-to-model matching. Through a viewport classification score, we rank reference panoramas and select the best match for the query image. Then, a 6D relative pose is estimated between the chosen panorama and query image. Our experiments demonstrate that this approach not only efficiently bridges the domain gap but also generalizes well to previously unseen scenes that are not part of the training data. Moreover, it achieves superior localization accuracy compared to the state of the art methods and also estimates more degrees of freedom of the camera pose. We will make our source code publicly available at https://github.com/fraunhoferhhi/spvloc .

本文介绍了SPVLoc，一种全球室内定位方法，能够准确确定查询图像的六维（6D）相机姿态，无需过多的现场特定先验知识和训练，采用了一种新颖的匹配过程，在室内环境的一组全景语义布局表示中定位了透视相机的视口，这些全景图由未贴图的3D参考模型渲染而成，仅包含关于房间形状的大致结构信息以及门窗标注。通过一个直接的卷积网络结构，我们展示了成功实现了图像到全景图以及图像到模型的匹配。通过视口分类得分，我们对参考全景图进行排名并选择最佳匹配的查询图像。然后，我们估计所选全景图与查询图像之间的六维相对姿态。我们的实验表明，这种方法不仅有效地弥合了领域差距，而且对先前未见过的训练数据之外的场景具有很好的泛化性能。此外，它在定位准确度上也比现有方法更优越，并且能估计更多自由度的相机姿态。我们将在此https URL上公开发布我们的源代码。

SPVLoc: 未见环境中的6D相机定位的语义全景视口匹配