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SGL: Structure Guidance Learning for Camera Localization

arXiv 2304.05571 ↗First posted 12 Apr 2023cs.CVcited by 0

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Abstract (arXiv)

Camera localization is a classical computer vision task that serves various Artificial Intelligence and Robotics applications. With the rapid developments of Deep Neural Networks (DNNs), end-to-end visual localization methods are prosperous in recent years. In this work, we focus on the scene coordinate prediction ones and propose a network architecture named as Structure Guidance Learning (SGL) which utilizes the receptive branch and the structure branch to extract both high-level and low-level features to estimate the 3D coordinates. We design a confidence strategy to refine and filter the predicted 3D observations, which enables us to estimate the camera poses by employing the Perspective-n-Point (PnP) with RANSAC. In the training part, we design the Bundle Adjustment trainer to help the network fit the scenes better. Comparisons with some state-of-the-art (SOTA) methods and sufficient ablation experiments confirm the validity of our proposed architecture.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 12 Apr 2023.

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31 of 47 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2017Squeeze-and-Excitation Networkscited by 4,763
  4. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  5. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  6. 2015NetVLAD: CNN architecture for weakly supervised place recognitioncited by 935
  7. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  8. 2020DISK: Learning local features with policy gradientcited by 310
  9. 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
  10. 2016DSAC - Differentiable RANSAC for Camera Localizationcited by 264
  11. 2016Large-Scale Image Retrieval with Attentive Deep Local Featurescited by 238
  12. 2017Geometric Loss Functions for Camera Pose Regression with Deep Learningcited by 224
19 more
  1. 2019R2D2: Repeatable and Reliable Detector and Descriptorcited by 206
  2. 2019Understanding the Limitations of CNN-based Absolute Camera Pose Regressioncited by 177
  3. 2020Visual Camera Re-Localization from RGB and RGB-D Images Using DSACcited by 176
  4. 2017Geometry-Aware Learning of Maps for Camera Localizationcited by 161
  5. 2017Learning Less is More - 6D Camera Localization via 3D Surface Regressioncited by 146
  6. 2016Image-based localization using LSTMs for structured feature correlationcited by 136
  7. 2015Modelling Uncertainty in Deep Learning for Camera Relocalizationcited by 133
  8. 2021Learning Multi-Scene Absolute Pose Regression with Transformerscited by 108
  9. 2017Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activationscited by 104
  10. 2019AtLoc: Attention Guided Camera Localizationcited by 92
  11. 2021On the Limits of Pseudo Ground Truth in Visual Camera Re-localisationcited by 63
  12. 2020Learning and aggregating deep local descriptors for instance-level recognitioncited by 54
  13. 2021VS-Net: Voting with Segmentation for Visual Localizationcited by 29
  14. 2021Learning Camera Localization via Dense Scene Matchingcited by 28
  15. 2016Random Forests versus Neural Networks - What's Best for Camera Localization?cited by 15
  16. 2021Pose Refinement with Joint Optimization of Visual Points and Linescited by 14
  17. 2018Full-Frame Scene Coordinate Regression for Image-Based Localizationcited by 13
  18. 2020Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesiscited by 10
  19. 2021Retrieval and Localization with Observation Constraintscited by 6

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