Image-Based Localization Using LSTMs for Structured Feature Correlation
Abstract
In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output, which play the role of a structured dimensionality reduction on the feature vector, leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based and SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces, where classic SIFT-based methods fail.
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Landmark-based localization consistently outperforms pose regression.
“However, landmark-based localization consistently outperforms pose regression [10, 54] due to CNNs performing pose regression via image retrieval instead of leveraging 3D structure [44].”
From Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering (2024) · cites this paper as [54]
Cites
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“Scene Area or Volume Active Search (w/o) [43] Active Search (w/) [43] PoseNet [23] Bayesian PoseNet [22] Proposed + Improvement(pos,ori)”
BackgroundMethodResultInfluentialGet Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization
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“The two datasets are very different from each other: 7Scenes has a very high number of images in a very small spatial extent, hence, it is more suited for applications such as Augmented Reality, while Cambridge Landmarks has sparser coverage and larger spatial extent, the perfect scenario for image-based localization.”
BackgroundInfluentialSelf-citation
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A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809 · see citing works
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
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Intraoperative 2D/3D Image Registration via Differentiable X-Ray Rendering
Gopalakrishnan, V., Dey, N., & Golland, P. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01108 · Cited by 48 · see citing works
“However, landmark-based localization consistently outperforms pose regression [10, 54] due to CNNs performing pose regression via image retrieval instead of leveraging 3D structure [44].”
Leveraging Equivariant Features for Absolute Pose Regression
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- Authors
- Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., & Cremers, D.
- Affiliations
- Technical University of Munich (DE)
- ETH Zurich (CH)
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 627-637
- OpenAlex
- W2584731199
- Cited by
- 521
- References
- 11
- Citations by year
- 2026: 17 · 2025: 39 · 2024: 63 · 2023: 58 · 2022: 72 · 2021: 63 · 2020: 89 · 2019: 78 · 2018: 37 · 2017: 5
- FWCI
- 100.06
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- image-based localization, feature correlation, dimensionality reduction, SIFT comparison, indoor positioning, outdoor localization, textureless surfaces, motion blur robustness, illumination variation
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