Outdoor visual localization is a crucial component to many computer vision systems. We propose an approach to localization from images that is designed to explicitly handle the strong variations in appearance happening between daytime and nighttime. As revealed by recent long-term localization benchmarks, both traditional feature-based and retrieval-based approaches still struggle to handle such changes. Our novel localization method combines a state-of-the-art image retrieval architecture with condition-specific sub-networks allowing the computation of global image descriptors that are explicitly dependent of the capturing conditions. We show that our approach improves localization by a factor of almost 300 compared to the popular VLAD-based methods on nighttime localization.
Feature-based methods achieve very competitive results in consistent daytime conditions @cite_5 @cite_2 @cite_56 @cite_38 @cite_11 . However, the pose estimation accuracy for such methods is heavily reliant on the local 2D-3D correspondences. Research in feature-based approaches mostly focuses on improving descriptor matching efficiency @cite_52 @cite_7 @cite_5 @cite_15 @cite_41 @cite_4 , speed @cite_50 @cite_59 and robustness @cite_33 @cite_36 @cite_8 @cite_69 @cite_2 @cite_47 . Despite these efforts, a significant number of erroneous matches appear under strong appearance variations. As a result, dramatic drops in localization accuracy are observed in harder conditions such as night-time @cite_10 .
@cite_38 @cite_69 @cite_47 @cite_4 @cite_33 @cite_7 @cite_8 @cite_41 @cite_36 @cite_52 @cite_56 @cite_50 @cite_2 @cite_59 @cite_5 @cite_15 @cite_10 @cite_11
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Image-based localization approaches aim to determine the camera pose from which an image was taken. Finding correct 2D-3D correspondences between query image features and 3D points in the scene model becomes harder as the size of the model increases. Current state-of-the-art methods therefore combine elaborate matching schemes with camera pose estimation techniques that are able to handle large fractions of wrong matches. In this work we study the benefits and limitations of spatial verification compared to appearance-based filtering. We propose a voting-based pose estimation strategy that exhibits O(n) complexity in the number of matches and thus facilitates to consider much more matches than previous approaches - whose complexity grows at least quadratically. This new outlier rejection formulation enables us to evaluate pose estimation for 1-to-many matches and to surpass the state-of-the-art. At the same time, we show that using more matches does not automatically lead to a better performance. To reliably determine the camera pose of an image relative to a 3D point cloud of a scene, correspondences between 2D features and 3D points are needed. Recent work has demonstrated that directly matching the features against the points outperforms methods that take an intermediate image retrieval step in terms of the number of images that can be localized successfully. Yet, direct matching is inherently less scalable than retrievalbased approaches. In this paper, we therefore analyze the algorithmic factors that cause the performance gap and identify false positive votes as the main source of the gap. Based on a detailed experimental evaluation, we show that retrieval methods using a selective voting scheme are able to outperform state-of-the-art direct matching methods. We explore how both selective voting and correspondence computation can be accelerated by using a Hamming embedding of feature descriptors. Furthermore, we introduce a new dataset with challenging query images for the evaluation of image-based localization. Regression-based models built on local gradient-based feature descriptors have showed to be successful for continuous pose estimation of object categories. Nonetheless, a crucial weakness of these methods is that no geometric information is taken into account. Therefore, geometrically inconsistent poses may be preferred, and this forces to employ a coarse-grained pose estimator as a pre-processing step to avoid potentially large estimation errors. In this paper, we propose a method that combines generative feature models and graph matching techniques in a unified probabilistic formulation of the continuous pose estimation problem. Our approach retains the lightness and generality of generative feature modeling, while favoring geometrically consistent results. Experiments show that pose pre-processing steps are not needed if geometry is embedded in the matching stage. We evaluated our approach on two different car datasets and we experimentally show that our algorithm outperforms state-of-the-art methods by 25 . We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching rather than local features to deal with textureless indoor scenes, and (iii) pose verification by virtual view synthesis to cope with significant changes in viewpoint, scene layout, and occluders. Second, we collect a new dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario. Third, we demonstrate that our method significantly outperforms current state-of-the-art indoor localization approaches on this new challenging data.