Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Sattler, Torsten; Maddern, Will; Toft, Carl; Torii, Akihiko; Hammarstrand, Lars; Stenborg, Erik; Safari, Daniel; Okutomi, Masatoshi; Pollefeys, Marc; Šivic, Josef; Kahl, Fredrik; Pajdla, Tomáš

doi:10.1109/cvpr.2018.00897

Cited by 592 publications

(773 citation statements)

References 84 publications

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“…Applying PCA does not improve the result and achieves lower accuracy on the course-precision regime due to the dimensionality reduction. Table II shows the comparison with several baselines, where the results of our baselines come form [20]. Our proposed methods achieve higher accuracy than baselines on the park part of the dataset in every precision regime.…”

Section: B Validation Of Difl and Fclmentioning

confidence: 99%

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

Wang

Liu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query images. However, in environments with drastically varying conditions (e.g. illumination changes, seasons, occlusion, dynamic objects), retrieval-based localization is severely hampered and becomes a challenging problem. In this paper, a novel domain-invariant feature learning method (DIFL) is proposed based on ComboGAN, a multi-domain image translation network architecture. By introducing a feature consistency loss (FCL) between the encoded features of the original image and translated image in another domain, we are able to train the encoders to generate domain-invariant features in a self-supervised manner. To retrieve a target image from the database, the query image is first encoded using the encoder belonging to the query domain to obtain a domain-invariant feature vector. We then preform retrieval by selecting the database image with the most similar domain-invariant feature vector. We validate the proposed approach on the CMU-Seasons dataset, where we outperform state-of-the-art learning-based descriptors in retrieval-based localization for high and medium precision scenarios.

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Section: B Validation Of Difl and Fclmentioning

confidence: 99%

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

Wang

Liu

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…As a result, traditional keypoint-based methods typically fail in such scenarios [22]. An alternative is to explicitly use edge and line information [12,15,24], but these meth- * Contributed equally.…”

Section: Introductionmentioning

confidence: 99%

Real-time camera pose estimation for sports fields

Citraro

Márquez-Neila

Savarè

et al. 2020

Machine Vision and Applications

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Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of a smartphone, our goal is to compute automatically and in real-time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera. To this end, we propose a novel framework that combines accurate localization and robust identification of specific keypoints in the image by using a fullyconvolutional deep architecture. Our algorithm exploits both the field lines and the players' image locations, assuming their ground plane positions to be given, to achieve accuracy and robustness that is beyond the current state of the art. We will demonstrate its effectiveness on challenging soccer, basketball, and volleyball benchmark datasets.

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“…computing the 6DOF camera pose for a query image, is a fundamental problem in many computer vision tasks. For example, IBL plays a key role in incremental Structure-from-Motion (SfM) reconstruction [13,36], visual place recognition [29], and visual navigation for autonomous vehicles [33]. IBL has witnessed tremendous advancement by means of deep learning [18,19] and image retrieval techniques [1,2,34].…”

Section: Introductionmentioning

confidence: 99%

Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization

Cheng

Wang

Chen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Image-based localization (IBL) aims to estimate the 6DOF camera pose for a given query image. The camera pose can be computed from 2D-3D matches between a query image and Structure-from-Motion (SfM) models. Despite recent advances in IBL, it remains difficult to simultaneously resolve the memory consumption and match ambiguity problems of large SfM models. In this work, we propose a cascaded parallel filtering method that leverages the feature, visibility and geometry information to filter wrong matches under binary feature representation. The core idea is that we divide the challenging filtering task into two parallel tasks before deriving an auxiliary camera pose for final filtering. One task focuses on preserving potentially correct matches, while another focuses on obtaining high quality matches to facilitate subsequent more powerful filtering. Moreover, our proposed method improves the localization accuracy by introducing a quality-aware spatial reconfiguration method and a principal focal length enhanced pose estimation method. Experimental results on real-world datasets demonstrate that our method achieves very competitive localization performances in a memoryefficient manner.

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Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions

Cited by 592 publications

References 84 publications

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments

Real-time camera pose estimation for sports fields

Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization

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