浏览全部资源
扫码关注微信
Received：23 October 2023，
Revised：2023-11-24，
Published：16 October 2024
移动端阅览
视觉定位旨在从已知的三维场景中恢复当前观测图像的相机位姿。视觉定位技术具备低成本、高精度和易于集成等优势，是实现计算设备与真实世界建立智能交互过程的关键技术之一，如今获得了混合现实、自动驾驶等应用领域的广泛关注。作为计算机视觉领域长期探索的基础任务之一，视觉定位方法至今已取得显著的研究进展，然而现有方法普遍存在计算开销和存储占用过大等不足，这些问题导致视觉定位在移动端的高效部署和场景模型的更新维护方面存在困难，并因此在很大程度上限制着视觉定位技术的实际应用。针对这一问题，部分研究工作开始聚焦于推动视觉定位技术的轻量化发展。轻量化视觉定位旨在研究更加高效的场景表达形式及其视觉定位方法，目前正逐渐成为视觉定位领域重要的研究方向。本文首先回顾早期视觉定位框架，随后从场景表达形式的角度对具备轻量化特性的现有视觉定位研究工作进行分类。在各个方法类别下，分析总结其特点优势、应用场景和技术难点，并同时介绍代表性成果。进一步地，本文对部分轻量化视觉定位的代表性方法在常用室内外数据集上的性能表现进行对比分析，评估指标主要包含离线建图的用时、场景地图的存储占用和定位精度3个维度。现有的轻量化视觉定位技术仍然面临着诸多的难题与挑战，场景模型的表达能力、定位方法的泛化性与鲁棒性尚存在较大的提升空间。最后，本文对轻量化视觉定位未来的发展趋势进行分析与展望。
Visual-based localization determines the camera translation and orientation of an image observation with respect to a prebuilt 3D-based representation of the environment. It is an essential technology that empowers the intelligent interactions between computing facilities and the real world. Compared with alternative positioning systems beyond， the capability to estimate the accurate 6DOF camera pose， along with the flexibility and frugality in deployment， positions visual-based localization technology as a cornerstone of many applications， ranging from autonomous vehicles to augmented and mixed reality. As a long-standing problem in computer vision， visual localization has made exceeding progress over the past decades. A primary branch of prior arts relies on a preconstructed 3D map obtained by structure-from-motion techniques. Such 3D maps， a.k.a. SfM point clouds， store 3D points and per-point visual features. To estimate the camera pose， these methods typically establish correspondences between 2D keypoints detected in the query image and 3D points of the SfM point cloud through descriptor matching. The 6DOF camera pose of the query image is then recovered from these 2D-3D matches by leveraging geometric principles introduced by photogrammetry. Despite delivering fairly sound and reliable performance， such a scheme often has to consume several gigabytes of storage for just a single scene， which would result in computationally expensive overhead and prohibitive memory footprint for large-scale applications and resource-intensive platforms. Furthermore， it suffers from other drawbacks， such as costly map maintenance and privacy vulnerability. The aforementioned issues pose a major bottleneck in real-world applications and have thus prompted researchers to shift their focus toward leaner solutions. Lightweight visual-based localization seeks to introduce improvements in scene representations and the associated localization methods， making the resulting framework computationally tractable and memory-efficient without incurring a notable performance expense. For the background， this literature review first introduces several flagship frameworks of the visual-based localization task as preliminaries. These frameworks can be broadly classified into three categories， including image-retrieval-based methods， structure-based methods， and hierarchical methods. 3D scene representations adopted in these conventional frameworks， such as reference image databases and SfM point clouds， generally exhibit a high degree of redundancy， which causes excessive memory usage and inefficiency in distinguishing scene features for descriptor matching. Next， this review provides a guided tour of recent advances that promote the brevity of the 3D scene representations and the efficiency of corresponding visual localization methods. From the perspective of scene representations， existing research efforts in lightweight visual localization can be classified into six categories. Within each category， this literature review analyzes its characteristics， application scenarios， and technical limitations while also surveying some of the representative works. First， several methods have been proposed to enhance memory efficiency by compressing the SfM point clouds. These methods reduce the size of SfM point clouds through the combination of techniques including feature quantization， keypoint subset sampling， and feature-free matching. Extreme compression rates， such as 1% and below， can be achieved with barely noticeable accuracy degradation. Employing line maps as scene representations has become a focus of research in the field of lightweight visual localization. In human-made scenes characterized by salient structural features， the substitution of line maps for point clouds offers two major merits： 1） the abundance and rich geometric properties of line segments make line maps a concise option for depicting the environment； 2） line features exhibit better robustness in weak-textured areas or under temporally varying lighting conditions. However， the lack of a unified line descriptor and the difficulty of establishing 2D-3D correspondences between 3D line segments and image observations remain as main challenges. In the field of autonomous driving， high-definition maps constructed from vectorized semantic features have unlocked a new wave of cost-effective and lightweight solutions to visual localization for self-driving vehicle. Recent trends involve the utilization of data-driven techniques to learn to localize. This end-to-end philosophy has given rise to two regression-based methods. Scene coordinate regression （SCR） methods eschew the explicit processes of feature extraction and matching. Instead， they establish a direct mapping between observations and scene coordinates through regression. While a grounding in geometry remains essential for camera pose estimation in SCR methods， pose regression methods employ deep neural networks to establish the mapping from image observations to camera poses without any explicit geometric reasoning. Absolute pose regression techniques are akin to image retrieval approaches with limited accuracy and generalization capability， while relative pose regression techniques typically serve as a postprocessing step following the coarse localization stage. Neural radiance fields and related volumetric-based approaches have emerged as a novel way for the neural implicit scene representation. While visual localization based solely on a learned volumetric-based implicit map is still in an exploratory phase， the progress made over the past year or two has already yielded an impressive performance in terms of the scene representation capability and precision of localization. Furthermore， this study quantitatively evaluates the performance of several representative lightweight visual localization methods on well-known indoor and outdoor datasets. Evaluation metrics， including offline mapping time usage， storage demand， and localization accuracy， are considered for making comparisons. Results reveal that SCR methods generally stand out among the existing work， boasting remarkably compact scene maps and high success rates of localization. Existing lightweight visual localization methods have dramatically pushed the performance boundary. However， challenges still remain in terms of scalability and robustness when enlarging the scene scale and taking considerable visual disparity between query and mapping images into consideration. Therefore， extensive efforts are still required to promote the compactness of scene representations and improving the robustness of localization methods. Finally， this review provides an outlook on developing trends in the hope of facilitating future research.
Arandjelovic R ， Gronat P ， Torii A ， Pajdla T and Sivic J . 2016 . NetVLAD： CNN architecture for weakly supervised place recognition // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 5297 - 5307 ［ DOI： 10.1109/CVPR.2016.572 http://dx.doi.org/10.1109/CVPR.2016.572 ］
Arandjelovic R ， Gronat P ， Torii A ， Pajdla T and Sivic J . 2018 . NetVLAD： CNN architecture for weakly supervised place recognition . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 40 （ 6 ）： 1437 - 1451 ［ DOI： 10.1109/TPAMI.2017.2711011 http://dx.doi.org/10.1109/TPAMI.2017.2711011 ］
Arnold E ， Wynn J ， Vicente S ， Garcia-Hernando G ， Monszpart Á ， Prisacariu V ， Turmukhambetov D and Brachmann E . 2022 . Map-free visual relocalization： metric pose relative to a single image // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 690 - 708 ［ DOI： 10.1007/978-3-031-19769-7_40 http://dx.doi.org/10.1007/978-3-031-19769-7_40 ］
Balntas V ， Li S D and Prisacariu V . 2018 . RelocNet： continuous metric learning relocalisation using neural nets // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 782 - 799 ［ DOI： 10.1007/978-3-030-01264-9_46 http://dx.doi.org/10.1007/978-3-030-01264-9_46 ］
Blanton H ， Greenwell C ， Workman S and Jacobs N . 2020 . Extending absolute pose regression to multiple scenes // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Seattle， USA ： IEEE： 170 - 178 ［ DOI： 10.1109/CVPRW50498.2020.00027 http://dx.doi.org/10.1109/CVPRW50498.2020.00027 ］
Brachmann E ， Cavallari T and Prisacariu V A . 2023 . Accelerated coordinate encoding： learning to relocalize in minutes using RGB and poses // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 5044 - 5053 ［ DOI： 10.1109/CVPR52729.2023.00488 http://dx.doi.org/10.1109/CVPR52729.2023.00488 ］
Brachmann E ， Krull A ， Nowozin S ， Shotton J ， Michel F ， Gumhold S and Rother C . 2017 . DSAC — differentiable RANSAC for camera localization // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 2492 - 2500 ［ DOI： 10.1109/CVPR.2017.267 http://dx.doi.org/10.1109/CVPR.2017.267 ］
Brachmann E ， Michel F ， Krull A ， Yang M Y ， Gumhold S and Rother C . 2016 . Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 3364 - 3372 ［ DOI： 10.1109/CVPR.2016.366 http://dx.doi.org/10.1109/CVPR.2016.366 ］
Brachmann E and Rother C . 2018 . Learning less is more - 6D camera localization via 3D surface regression // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 4654 - 4662 ［ DOI： 10.1109/CVPR.2018.00489 http://dx.doi.org/10.1109/CVPR.2018.00489 ］
Brachmann E and Rother C . 2019a . Neural-guided RANSAC： learning where to sample model hypotheses // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 4321 - 4330 ［ DOI： 10.1109/ICCV.2019.00442 http://dx.doi.org/10.1109/ICCV.2019.00442 ］
Brachmann E and Rother C . 2019b . Expert sample consensus applied to camera re-localization // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 7524 - 7533 ［ DOI： 10.1109/ICCV.2019.00762 http://dx.doi.org/10.1109/ICCV.2019.00762 ］
Brachmann E and Rother C . 2022 . Visual camera re-localization from RGB and RGB-D images using DSAC . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 44 （ 9 ）： 5847 - 5865 ［ DOI： 10.1109/TPAMI.2021.3070754 http://dx.doi.org/10.1109/TPAMI.2021.3070754 ］
Brahmbhatt S ， Gu J W ， Kim K ， Hays J and Kautz J . 2018 . Geometry-aware learning of maps for camera localization // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 2616 - 2625 ［ DOI： 10.1109/CVPR.2018.00277 http://dx.doi.org/10.1109/CVPR.2018.00277 ］
Brown M ， Windridge D and Guillemaut J Y . 2015 . Globally optimal 2D-3D registration from points or lines without correspondences // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 2111 - 2119 ［ DOI： 10.1109/ICCV.2015.244 http://dx.doi.org/10.1109/ICCV.2015.244 ］
Bui M ， Baur C ， Navab N ， Ilic S and Albarqouni S . 2019 . Adversarial networks for camera pose regression and refinement // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop . Seoul， Korea （South） ： IEEE： 3778 - 3787 ［ DOI： 10.1109/ICCVW.2019.00470 http://dx.doi.org/10.1109/ICCVW.2019.00470 ］
Cai R J ， Hariharan B ， Snavely N and Averbuch-Elor H . 2021 . Extreme rotation estimation using dense correlation volumes // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 14561 - 14570 ［ DOI： 10.1109/CVPR46437.2021.01433 http://dx.doi.org/10.1109/CVPR46437.2021.01433 ］
Campbell D ， Liu L and Gould S . 2020a . Solving the blind perspective-n-point problem end-to-end with robust differentiable geometric optimization // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 244 - 261 ［ DOI： 10.1007/978-3-030-58536-5_15 http://dx.doi.org/10.1007/978-3-030-58536-5_15 ］
Campbell D ， Petersson L ， Kneip L and Li H D . 2020b . Globally-optimal inlier set maximisation for camera pose and correspondence estimation . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 42 （ 2 ）： 328 - 342 ［ DOI： 10.1109/TPAMI.2018.2848650 http://dx.doi.org/10.1109/TPAMI.2018.2848650 ］
Campbell D ， Petersson L ， Kneip L ， Li H D and Gould S . 2019 . The alignment of the spheres： globally-optimal spherical mixture alignment for camera pose estimation // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 11788 - 11798 ［ DOI： 10.1109/CVPR.2019.01207 http://dx.doi.org/10.1109/CVPR.2019.01207 ］
Camposeco F ， Cohen A ， Pollefeys M and Sattler T . 2019 . Hybrid scene compression for visual localization // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 7645 - 7654 ［ DOI： 10.1109/CVPR.2019.00784 http://dx.doi.org/10.1109/CVPR.2019.00784 ］
Camposeco F ， Sattler T ， Cohen A ， Geiger A and Pollefeys M . 2017 . Toroidal constraints for two-point localization under high outlier ratios // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 6700 - 6708 ［ DOI： 10.1109/CVPR.2017.709 http://dx.doi.org/10.1109/CVPR.2017.709 ］
Cao S and Snavely N . 2014 . Minimal scene descriptions from structure from motion models // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus， USA ： IEEE： 461 - 468 ［ DOI： 10.1109/CVPR.2014.66 http://dx.doi.org/10.1109/CVPR.2014.66 ］
Carion N ， Massa F ， Synnaeve G ， Usunier N ， Kirillov A and Zagoruyko S . 2020 . End-to-end object detection with Transformers // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 213 - 229 ［ DOI： 10.1007/978-3-030-58452-8_13 http://dx.doi.org/10.1007/978-3-030-58452-8_13 ］
Cavallari T ， Bertinetto L ， Mukhoti J ， Torr P and Golodetz S . 2019 . Let’s take this online： adapting scene coordinate regression network predictions for online RGB-D camera relocalisation // Proceedings of 2019 International Conference on 3D Vision . Québec City， Canada ： IEEE： 564 - 573 ［ DOI： 10.1109/3DV.2019.00068 http://dx.doi.org/10.1109/3DV.2019.00068 ］
Cavallari T ， Golodetz S ， Lord N A ， Valentin J ， di Stefano L and Torr P H S . 2017 . On-the-fly adaptation of regression forests for online camera relocalisation // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 218 - 227 ［ DOI： 10.1109/CVPR.2017.31 http://dx.doi.org/10.1109/CVPR.2017.31 ］
Cavallari T ， Golodetz S ， Lord N A ， Valentin J ， Prisacariu V A ， Stefano L D and Torr P H S . 2020 . Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 42 （ 10 ）： 2465 - 2477 ［ DOI： 10.1109/TPAMI.2019.2915068 http://dx.doi.org/10.1109/TPAMI.2019.2915068 ］
Chen K F ， Snavely N and Makadia A . 2021 . Wide-baseline relative camera pose estimation with directional learning // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 3257 - 3267 ［ DOI： 10.1109/CVPR46437.2021.00327 http://dx.doi.org/10.1109/CVPR46437.2021.00327 ］
Chen L C ， Zhu Y K ， Papandreou G ， Schroff F and Adam H . 2018 . Encoder-decoder with atrous separable convolution for semantic image segmentation // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 833 - 851 ［ DOI： 10.1007/978-3-030-01234-2_49 http://dx.doi.org/10.1007/978-3-030-01234-2_49 ］
Chen S ， Li X H ， Wang Z R and Prisacariu V A . 2022 . DFNet： enhance absolute pose regression with direct feature matching // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 1 - 17 ［ DOI： 10.1007/978-3-031-20080-9_1 http://dx.doi.org/10.1007/978-3-031-20080-9_1 ］
Chen Y ， Chen X Y ， Wang X ， Zhang Q ， Guo Y ， Shan Y and Wang F . 2023 . Local-to-global registration for bundle-adjusting neural radiance fields // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 8264 - 8273 ［ DOI： 10.1109/CVPR52729.2023.00799 http://dx.doi.org/10.1109/CVPR52729.2023.00799 ］
Chen Z H ， Pei H Y ， Wang J K and Dai D Y . 2021 . Survey of monocular camera-based visual relocalization . Robot ， 43 （ 3 ）： 373 - 384
陈宗海 ， 裴浩渊 ， 王纪凯 ， 戴德云 . 2021 . 基于单目相机的视觉重定位方法综述 . 机器人 ， 43 （ 3 ）： 373 - 384 ［ DOI： 10.13973/j.cnki.robot.200350 http://dx.doi.org/10.13973/j.cnki.robot.200350 ］
Cheng W T ， Lin W S ， Chen K and Zhang X F . 2019 . Cascaded parallel filtering for memory-efficient image-based localization // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 1032 - 1041 ［ DOI： 10.1109/ICCV.2019.00112 http://dx.doi.org/10.1109/ICCV.2019.00112 ］
Chidlovskii B and Sadek A . 2020 . Adversarial transfer of pose estimation regression // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer： 646 - 661 ［ DOI： 10.1007/978-3-030-66415-2_43 http://dx.doi.org/10.1007/978-3-030-66415-2_43 ］
Cipolla R ， Gal Y and Kendall A . 2018 . Multi-task learning using uncertainty to weigh losses for scene geometry and semantics // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 7482 - 7491 ［ DOI： 10.1109/CVPR.2018.00781 http://dx.doi.org/10.1109/CVPR.2018.00781 ］
Clark R ， Wang S ， Markham A ， Trigoni N and Wen H K . 2017 . VidLoc： a deep spatio-temporal model for 6-DoF video-clip relocalization // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 2652 - 2660 ［ DOI： 10.1109/CVPR.2017.284 http://dx.doi.org/10.1109/CVPR.2017.284 ］
Dai A ， Nießner M ， Zollhöfer M ， Izadi S and Theobalt C . 2017 . BundleFusion： real-time globally consistent 3D reconstruction using on-the-fly surface reintegration . ACM Transactions on Graphics ， 36 （ 3 ）： # 24 ［ DOI： 10.1145/3054739 http://dx.doi.org/10.1145/3054739 ］
David P ， DeMenthon D ， Duraiswami R and Samet H . 2004 . SoftPOSIT： simultaneous pose and correspondence determination . International Journal of Computer Vision ， 59 （ 3 ）： 259 - 284 ［ DOI： 10.1023/B：VISI.0000025800.10423.1f http://dx.doi.org/10.1023/B：VISI.0000025800.10423.1f ］
DeTone D ， Malisiewicz T and Rabinovich A . 2018 . SuperPoint： self-supervised interest point detection and description // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Salt Lake City， USA ： IEEE： 337 - 349 ［ DOI： 10.1109/CVPRW.2018.00060 http://dx.doi.org/10.1109/CVPRW.2018.00060 ］
Ding M Y ， Wang Z ， Sun J K ， Shi J P and Luo P . 2019 . CamNet： coarse-to-fine retrieval for camera re-localization // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 2871 - 2880 ［ DOI： 10.1109/ICCV.2019.00296 http://dx.doi.org/10.1109/ICCV.2019.00296 ］
Dong S Y ， Wang S Z ， Zhuang Y X ， Kannala J ， Pollefeys M and Chen B Q . 2022 . Visual localization via few-shot scene region classification // Proceedings of 2022 International Conference on 3D Vision . Prague， Czech Republic ： IEEE： 393 - 402 ［ DOI： 10.1109/3DV57658.2022.00051 http://dx.doi.org/10.1109/3DV57658.2022.00051 ］
Dong S Y ， Liu H ， Guo H K ， Chen B Q and Pollefeys M . 2023 . Lazy visual localization via motion averaging ［EB/OL］. ［ 2023-08-19 ］. http://arxiv.org/pdf/2307.09981.pdf http://arxiv.org/pdf/2307.09981.pdf
Donoser M and Schmalstieg D . 2014 . Discriminative feature-to-point matching in image-based localization // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus， USA ： IEEE： 516 - 523 ［ DOI： 10.1109/CVPR.2014.73 http://dx.doi.org/10.1109/CVPR.2014.73 ］
Fang Q H ， Yin Y D ， Fan Q N ， Xia F ， Dong S Y ， Wang S ， Wang J ， Guibas L J and Chen B Q . 2022 . Towards accurate active camera localization // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 122 - 139 ［ DOI： 10.1007/978-3-031-20080-9_8 http://dx.doi.org/10.1007/978-3-031-20080-9_8 ］
Fischler M A and Bolles R C . 1981 . Random sample consensus： a paradigm for model fitting with applications to image analysis and automated cartography . Communications of the ACM ， 24 （ 6 ）： 381 - 395 ［ DOI： 10.1145/358669.358692 http://dx.doi.org/10.1145/358669.358692 ］
Gao S ， Wan J X ， Ping Y S ， Zhang X D ， Dong S Z ， Yang Y C ， Ning H K ， Li J J N and Guo Y D . 2022 . Pose refinement with joint optimization of visual points and lines // Proceedings of 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems . Kyoto， Japan ： IEEE： 2888 - 2894 ［ DOI： 10.1109/IROS47612.2022.9981420 http://dx.doi.org/10.1109/IROS47612.2022.9981420 ］
Gong R H ， Liu X L ， Jiang S H ， Li T X ， Hu P ， Lin J Z ， Yu F W and Yan J J . 2019 . Differentiable soft quantization： bridging full-precision and low-bit neural networks // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 4851 - 4860 ［ DOI： 10.1109/ICCV.2019.00495 http://dx.doi.org/10.1109/ICCV.2019.00495 ］
Goto T ， Pathak S ， Ji Y ， Fujii H ， Yamashita A and Asama H . 2018 . Line-based global localization of a spherical camera in manhattan worlds // Proceedings of 2018 IEEE International Conference on Robotics and Automation . Brisbane， Australia ： IEEE： 2296 - 2303 ［ DOI： 10.1109/ICRA.2018.8460920 http://dx.doi.org/10.1109/ICRA.2018.8460920 ］
Guzman-Rivera A ， Kohli P ， Glocker B ， Shotton J ， Sharp T ， Fitzgibbon A and Izadi S . 2014 . Multi-output learning for camera relocalization // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus， USA ： IEEE： 1114 - 1121 ［ DOI： 10.1109/CVPR.2014.146 http://dx.doi.org/10.1109/CVPR.2014.146 ］
Hofer M ， Maurer M and Bischof H . 2017 . Efficient 3D scene abstraction using line segments . Computer Vision and Image Understanding ， 157 ： 167 - 178 ［ DOI： 10.1016/j.cviu.2016.03.017 http://dx.doi.org/10.1016/j.cviu.2016.03.017 ］
Huang Z Y ， Zhou H ， Li Y J ， Yang B B ， Xu Y ， Zhou X W ， Bao H J ， Zhang G F and Li H S . 2021 . VS-net： voting with segmentation for visual localization // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 6097 - 6107 ［ DOI： 10.1109/CVPR46437.2021.00604 http://dx.doi.org/10.1109/CVPR46437.2021.00604 ］
Irschara A ， Zach C ， Frahm J M and Bischof H . 2009 . From structure-from-motion point clouds to fast location recognition // Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition . Miami， USA ： IEEE： 2599 - 2606 ［ DOI： 10.1109/CVPR.2009.5206587 http://dx.doi.org/10.1109/CVPR.2009.5206587 ］
Izadi S ， Kim D ， Hilliges O ， Molyneaux D ， Newcombe R ， Kohli P ， Shotton J ， Hodges S ， Freeman D ， Davison A and Fitzgibbon A . 2011 . KinectFusion： real-time 3D reconstruction and interaction using a moving depth camera // Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology . Santa Barbara， USA ： ACM： 559 - 568 ［ DOI： 10.1145/2047196.2047270 http://dx.doi.org/10.1145/2047196.2047270 ］
Jégou H ， Douze M ， Schmid C and Pérez P . 2010 . Aggregating local descriptors into a compact image representation // Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . San Francisco， USA ： IEEE： 3304 - 3311 ［ DOI： 10.1109/CVPR.2010.5540039 http://dx.doi.org/10.1109/CVPR.2010.5540039 ］
Jeong J ， Cho Y and Kim A . 2020 . HDMI-Loc： exploiting high definition map image for precise localization via bitwise particle filter . IEEE Robotics and Automation Letters ， 5 （ 4 ）： 6310 - 6317 ［ DOI： 10.1109/LRA.2020.3013881 http://dx.doi.org/10.1109/LRA.2020.3013881 ］
Kabsch W . 1976 . A solution for the best rotation to relate two sets of vectors . Acta Crystallographica Section A ， 32 （ 5 ）： 922 - 923 ［ DOI： 10.1107/S0567739476001873 http://dx.doi.org/10.1107/S0567739476001873 ］
Kendall A and Cipolla R . 2016 . Modelling uncertainty in deep learning for camera relocalization // Proceedings of 2016 IEEE International Conference on Robotics and Automation . Stockholm， Sweden ： IEEE： 4762 - 4769 ［ DOI： 10.1109/ICRA.2016.7487679 http://dx.doi.org/10.1109/ICRA.2016.7487679 ］
Kendall A and Cipolla R . 2017 . Geometric loss functions for camera pose regression with deep learning // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 6555 - 6564 ［ DOI： 10.1109/CVPR.2017.694 http://dx.doi.org/10.1109/CVPR.2017.694 ］
Kendall A ， Grimes M and Cipolla R . 2015 . PoseNet： a convolutional network for real-time 6-DOF camera relocalization // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 2938 - 2946 ［ DOI： 10.1109/ICCV.2015.336 http://dx.doi.org/10.1109/ICCV.2015.336 ］
Kim J ， Choi C ， Jang H and Kim Y . 2023 . LDL： line distance functions for panoramic localization // Proceedings of 2023 IEEE/CVF International Conference on Computer Vision . Paris， France ： IEEE： 17836 - 17846 ［ DOI： 10.1109/ICCV51070.2023.01639 http://dx.doi.org/10.1109/ICCV51070.2023.01639 ］
Krull A ， Brachmann E ， Michel F ， Yang M Y ， Gumhold S and Rother C . 2015 . Learning analysis-by-synthesis for 6D pose estimation in RGB-D images // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 954 - 962 ［ DOI： 10.1109/ICCV.2015.115 http://dx.doi.org/10.1109/ICCV.2015.115 ］
Laskar Z ， Melekhov I ， Kalia S and Kannala J . 2017 . Camera relocalization by computing pairwise relative poses using convolutional neural network // Proceedings of 2017 IEEE International Conference on Computer Vision Workshops . Venice， Italy ： IEEE： 920 - 929 ［ DOI： 10.1109/ICCVW.2017.113 http://dx.doi.org/10.1109/ICCVW.2017.113 ］
Lepetit V ， Moreno-Noguer F and Fua P . 2009 . EP n P： an accurate O （ n ） solution to the P n P problem . International Journal of Computer Vision ， 81 （ 2 ）： 155 - 166 ［ DOI： 10.1007/s11263-008-0152-6 http://dx.doi.org/10.1007/s11263-008-0152-6 ］
Li X T ， Wang S Z ， Zhao Y ， Verbeek J and Kannala J . 2020 . Hierarchical scene coordinate classification and regression for visual localization // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 11980 - 11989 ［ DOI： 10.1109/cvpr42600.2020.01200 http://dx.doi.org/10.1109/cvpr42600.2020.01200 ］
Li X T ， Ylioinas J ， Verbeek J and Kannala J . 2019 . Scene coordinate regression with angle-based reprojection loss for camera relocalization // Proceedings of the 15th European Conference on Computer Vision Workshops . Munich， Germany ： Springer： 229 - 245 ［ DOI： 10.1007/978-3-030-11015-4_19 http://dx.doi.org/10.1007/978-3-030-11015-4_19 ］
Li Y P ， Snavely N ， Huttenlocher D and Fua P . 2012 . Worldwide pose estimation using 3D point clouds // Proceedings of the 12th European Conference on Computer Vision . Florence， Italy ： Springer： 15 - 29 ［ DOI： 10.1007/978-3-642-33718-5_2 http://dx.doi.org/10.1007/978-3-642-33718-5_2 ］
Li Y P ， Snavely N and Huttenlocher D P . 2010 . Location recognition using prioritized feature matching // Proceedings of the 11th European Conference on Computer Vision . Heraklion， Greece ： Springer： 791 - 804 ［ DOI： 10.1007/978-3-642-15552-9_57 http://dx.doi.org/10.1007/978-3-642-15552-9_57 ］
Liao W L ， Zhao H Q and Yan J C . 2021 . Online extrinsic camera calibration based on high-definition map matching on public roadway . Journal of Image and Graphics ， 26 （ 1 ）： 208 - 217
廖文龙 ， 赵华卿 ， 严骏驰 . 2021 . 开放道路中匹配高精度地图的在线相机外参标定 . 中国图象图形学报 ， 26 （ 1 ）： 208 - 217 ［ DOI： 10.11834/jig.200432 http://dx.doi.org/10.11834/jig.200432 ］
Lin C H ， Ma W C ， Torralba A and Lucey S . 2021 . BARF： bundle-adjusting neural radiance fields // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 5721 - 5731 ［ DOI： 10.1109/ICCV48922.2021.00569 http://dx.doi.org/10.1109/ICCV48922.2021.00569 ］
Lin Y Z ， Müller T ， Tremblay J ， Wen B W ， Tyree S ， Evans A ， Vela P A and Birchfield S . 2023 . Parallel inversion of neural radiance fields for robust pose estimation // Proceedings of 2023 IEEE International Conference on Robotics and Automation . London， United Kingdom ： IEEE： 9377 - 9384 ［ DOI： 10.1109/ICRA48891.2023.10161117 http://dx.doi.org/10.1109/ICRA48891.2023.10161117 ］
Lindeberg T . 1998 . Edge detection and ridge detection with automatic scale selection . International Journal of Computer Vision ， 30 （ 2 ）： 117 - 156 ［ DOI： 10.1023/A：1008097225773 http://dx.doi.org/10.1023/A：1008097225773 ］
Lindenberger P ， Sarlin P-E and Pollefeys M . 2023 . LightGlue： local feature matching at light speed // Proceedings of 2023 IEEE/CVF International Conference on Computer Vision . Paris， France ： IEEE： 17581 – 17592 ［ DOI： 10.1109/ICCV51070.2023.01616 http://dx.doi.org/10.1109/ICCV51070.2023.01616 ］
Liu J L ， Nie Q ， Liu Y and Wang C J . 2023a . NeRF-loc： visual localization with conditional neural radiance field // Proceedings of 2023 IEEE International Conference on Robotics and Automation . London， United Kingdom ： IEEE： 9385 - 9392 ［ DOI： 10.1109/ICRA48891.2023.10161420 http://dx.doi.org/10.1109/ICRA48891.2023.10161420 ］
Liu L ， Li H D and Dai Y C . 2017 . Efficient global 2D-3D matching for camera localization in a large-scale 3D map // Proceedings of 2017 IEEE International Conference on Computer Vision . Venice， Italy ： IEEE： 2391 - 2400 ［ DOI： 10.1109/ICCV.2017.260 http://dx.doi.org/10.1109/ICCV.2017.260 ］
Liu S ， Zhang Y X ， Xu J T ， Zou D F ， Chen S Y and Wang Z H . 2020 . Visual prior-information-based map recovery slam in complex scenes . Journal of Image and Graphics ， 25 （ 1 ）： 158 - 170
刘盛 ， 张宇翔 ， 徐婧婷 ， 邹大方 ， 陈胜勇 ， 王振华 . 2020 . 复杂场景下视觉先验信息的地图恢复SLAM . 中国图象图形学报 ， 25 （ 1 ）： 158 - 170 ［ DOI： 10.11834/jig.190041 http://dx.doi.org/10.11834/jig.190041 ］
Liu S H ， Yu Y F ， Pautrat R ， Pollefeys M and Larsson V . 2023b . 3D line mapping revisited // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 21445 - 21455 ［ DOI： 10.1109/CVPR52729.2023.02054 http://dx.doi.org/10.1109/CVPR52729.2023.02054 ］
Lowe D G . 2004 . Distinctive image features from scale-invariant keypoints . International Journal of Computer Vision ， 60 （ 2 ）： 91 - 110 ［ DOI： 10.1023/B：VISI.0000029664.99615.94 http://dx.doi.org/10.1023/B：VISI.0000029664.99615.94 ］
Lowry S ， Sünderhauf N ， Newman P ， Leonard J J ， Cox D ， Corke P and Milford M J . 2016 . Visual place recognition： a survey . IEEE Transactions on Robotics ， 32 （ 1 ）： 1 - 19 ［ DOI： 10.1109/TRO.2015.2496823 http://dx.doi.org/10.1109/TRO.2015.2496823 ］
Lu Y ， Huang J W ， Chen Y T and Heisele B . 2017 . Monocular localization in urban environments using road markings // Proceedings of 2017 IEEE Intelligent Vehicles Symposium . Los Angeles， USA ： IEEE： 468 - 474 ［ DOI： 10.1109/IVS.2017.7995762 http://dx.doi.org/10.1109/IVS.2017.7995762 ］
Maggio D ， Abate M ， Shi J N ， Mario C and Carlone L . 2023 . Loc-NeRF： Monte Carlo localization using neural radiance fields // Proceedings of 2023 IEEE International Conference on Robotics and Automation . London， United Kingdom ： IEEE： 4018 - 4025 ［ DOI： 10.1109/ICRA48891.2023.10160782 http://dx.doi.org/10.1109/ICRA48891.2023.10160782 ］
Melekhov I ， Ylioinas J ， Kannala J and Rahtu E . 2017 . Image-based localization using hourglass networks // Proceedings of 2017 IEEE International Conference on Computer Vision Workshops . Venice， Italy ： IEEE： 870 - 877 ［ DOI： 10.1109/ICCVW.2017.107 http://dx.doi.org/10.1109/ICCVW.2017.107 ］
Meng Q ， Chen A P ， Luo H M ， Wu M Y ， Su H ， Xu L ， He X M and Yu J Y . 2021 . GNeRF： GAN-based neural radiance field without posed camera // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 6331 - 6341 ［ DOI： 10.1109/ICCV48922.2021.00629 http://dx.doi.org/10.1109/ICCV48922.2021.00629 ］
Mera-Trujillo M ， Smith B and Fragoso V . 2020 . Efficient scene compression for visual-based localization // Proceedings of 2020 International Conference on 3D Vision . Fukuoka， Japan ： IEEE： 1 - 10 ［ DOI： 10.1109/3DV50981.2020.00111 http://dx.doi.org/10.1109/3DV50981.2020.00111 ］
Micusík B and Wildenauer H . 2015 . Descriptor free visual indoor localization with line segments // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 3165 - 3173 ［ DOI： 10.1109/CVPR.2015.7298936 http://dx.doi.org/10.1109/CVPR.2015.7298936 ］
Micusik B and Wildenauer H . 2017 . Structure from motion with line segments under relaxed endpoint constraints . International Journal of Computer Vision ， 124 （ 1 ）： 65 - 79 ［ DOI： 10.1007/s11263-016-0971-9 http://dx.doi.org/10.1007/s11263-016-0971-9 ］
Middelberg S ， Sattler T ， Untzelmann O and Kobbelt L . 2014 . Scalable 6-DOF localization on mobile devices // Proceedings of the 13th European Conference on Computer Vision . Zurich， Switzerland ： Springer： 268 - 283 ［ DOI： 10.1007/978-3-319-10605-2_18 http://dx.doi.org/10.1007/978-3-319-10605-2_18 ］
Mildenhall B ， Srinivasan P P ， Tancik M ， Barron J T ， Ramamoorthi R and Ng R . 2020 . NeRF： representing scenes as neural radiance fields for view synthesis // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： Springer International Publishing： 405 - 421 ［ DOI： 10.1007/978-3-030-58452-8_24 http://dx.doi.org/10.1007/978-3-030-58452-8_24 ］
Moreau A ， Piasco N ， Bennehar M ， Tsishkou D ， Stanciulescu B and de La Fortelle A . 2023 . CROSSFIRE： camera relocalization on self-supervised features from an implicit representation // Proceedings of 2023 IEEE/CVF International Conference on Computer Vision . Paris， France ： IEEE： 252 - 262 ［ DOI： 10.1109/ICCV51070.2023.00030 http://dx.doi.org/10.1109/ICCV51070.2023.00030 ］
Moreau A ， Piasco N ， Tsishkou D ， Stanciulescu B and de La Fortelle A . 2022 . LENS： localization enhanced by NeRF synthesis // Proceedings of the 5th Conference on Robot Learning . Auckland， New Zealand ： PMLR： 1347 - 1356
Moreno-Noguer F ， Lepetit V and Fua P . 2008 . Pose priors for simultaneously solving alignment and correspondence // Proceedings of the 10th European Conference on Computer Vision . Marseille， France ： Springer： 405 - 418 ［ DOI： 10.1007/978-3-540-88688-4_30 http://dx.doi.org/10.1007/978-3-540-88688-4_30 ］
Mur-Artal R ， Montiel J M M and Tardós J D . 2015 . ORB-SLAM： a versatile and accurate monocular SLAM system . IEEE Transactions on Robotics ， 31 （ 5 ）： 1147 - 1163 ［ DOI： 10.1109/TRO.2015.2463671 http://dx.doi.org/10.1109/TRO.2015.2463671 ］
Naseer T and Burgard W . 2017 . Deep regression for monocular camera-based 6-DoF global localization in outdoor environments // Proceedings of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems . Vancouver， Canada ： IEEE： 1525 - 1530 ［ DOI： 10.1109/IROS.2017.8205957 http://dx.doi.org/10.1109/IROS.2017.8205957 ］
Newcombe R A ， Fitzgibbon A ， Izadi S ， Hilliges O ， Molyneaux D ， Kim D ， Davison A J ， Kohi P ， Shotton J and Hodges S . 2011 . KinectFusion： real-time dense surface mapping and tracking // Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality . Basel， Switzerland ： IEEE： 127 - 136 ［ DOI： 10.1109/ISMAR.2011.6092378 http://dx.doi.org/10.1109/ISMAR.2011.6092378 ］
Nichol A ， Achiam J and Schulman J . 2018 . On first-order meta-learning algorithms ［EB/OL］. ［ 2023-10-23 ］. https://arxiv.org/pdf/1803.02999v3.pdf https://arxiv.org/pdf/1803.02999v3.pdf
Nistér D . 2003 . Preemptive RANSAC for live structure and motion estimation // Proceedings of the 9th IEEE International Conference on Computer Vision . Nice， France ： IEEE： 199 - 206 ［ DOI： 10.1109/ICCV.2003.1238341 http://dx.doi.org/10.1109/ICCV.2003.1238341 ］
Ozuysal M ， Calonder M ， Lepetit V and Fua P . 2010 . Fast keypoint recognition using random ferns . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 32 （ 3 ）： 448 - 461 ［ DOI： 10.1109/TPAMI.2009.23 http://dx.doi.org/10.1109/TPAMI.2009.23 ］
Pan X K ， Liu H M ， Fang M ， Wang Z ， Zhang Y and Zhang G F . 2023 . Dynamic 3D scenario-oriented monocular slam based on semantic probability prediction . Journal of Image and Graphics ， 28 （ 7 ）： 2151 - 2166
潘小鹍 ， 刘浩敏 ， 方铭 ， 王政 ， 张涌 ， 章国锋 . 2023 . 基于语义概率预测的动态场景单目视觉SLAM . 中国图象图形学报 ， 28 （ 7 ）： 2151 - 2166 ［ DOI： 10.11834/jig.210632 http://dx.doi.org/10.11834/jig.210632 ］
Panek V ， Kukelova Z and Sattler T . 2023 . Visual localization using imperfect 3D models from the internet // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 13175 - 13186 ［ DOI： 10.1109/CVPR52729.2023.01266 http://dx.doi.org/10.1109/CVPR52729.2023.01266 ］
Park H S ， Wang Y ， Nurvitadhi E ， Hoe J C ， Sheikh Y and Chen M . 2013 . 3D point cloud reduction using mixed-integer quadratic programming // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops . Portland， USA ： IEEE： 229 - 236 ［ DOI： 10.1109/CVPRW.2013.41 http://dx.doi.org/10.1109/CVPRW.2013.41 ］
Perez E ， Strub F ， De Vries H ， Dumoulin V and Courville A . 2018 . FiLM： visual reasoning with a general conditioning layer // Proceedings of the 32nd AAAI Conference on Artificial Intelligence . New Orleans， USA ： AAAI Press： 3942 - 3951 ［ DOI： 10.1609/aaai.v32i1.11671 http://dx.doi.org/10.1609/aaai.v32i1.11671 ］
Poggenhans F ， Salscheider N O and Stiller C . 2018 . Precise localization in high-definition road maps for urban regions // Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems . Madrid， Spain ： IEEE： 2167 - 2174 ［ DOI： 10.1109/IROS.2018.8594414 http://dx.doi.org/10.1109/IROS.2018.8594414 ］
Radwan N ， Valada A and Burgard W . 2018 . VLocNet++： deep multitask learning for semantic visual localization and odometry . IEEE Robotics and Automation Letters ， 3 （ 4 ）： 4407 - 4414 ［ DOI： 10.1109/LRA.2018.2869640 http://dx.doi.org/10.1109/LRA.2018.2869640 ］
Ranganathan A ， Ilstrup D and Wu T . 2013 . Light-weight localization for vehicles using road markings // Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . Tokyo， Japan ： IEEE： 921 - 927 ［ DOI： 10.1109/IROS.2013.6696460 http://dx.doi.org/10.1109/IROS.2013.6696460 ］
Revaud J ， Weinzaepfel P ， De Souza C and Humenberger M . 2019 . R 2 D 2 ： repeatable and reliable detector and descriptor // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Vancouver， Canada ： Curran Associates Inc.： 12414 - 12424
Rublee E ， Rabaud V ， Konolige K and Bradski G . 2011 . ORB： an efficient alternative to SIFT or SURF // Proceedings of 2011 IEEE International Conference on Computer Vision . Barcelona， Spain ： IEEE： 2564 - 2571 ［ DOI： 10.1109/ICCV.2011.6126544 http://dx.doi.org/10.1109/ICCV.2011.6126544 ］
Sandler M ， Howard A ， Zhu M L ， Zhmoginov A and Chen L C . 2018 . MobileNetV2： inverted residuals and linear bottlenecks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 4510 - 4520 ［ DOI： 10.1109/CVPR.2018.00474 http://dx.doi.org/10.1109/CVPR.2018.00474 ］
Sarlin P E ， Cadena C ， Siegwart R and Dymczyk M . 2019 . From coarse to fine： robust hierarchical localization at large scale // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 12708 - 12717 ［ DOI： 10.1109/CVPR.2019.01300 http://dx.doi.org/10.1109/CVPR.2019.01300 ］
Sarlin P E ， Debraine F ， Dymczyk M ， Siegwart R and Cadena C . 2018 . Leveraging deep visual descriptors for hierarchical efficient localization // Proceedings of the 2nd Conference on Robot Learning . Zürich， Switzerland ： PMLR： 456 - 465 ［ DOI： 10.3929/ETHZ-B-000318818 http://dx.doi.org/10.3929/ETHZ-B-000318818 ］
Sarlin P E ， DeTone D ， Malisiewicz T and Rabinovich A . 2020 . SuperGlue： learning feature matching with graph neural networks // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle， USA ： IEEE： 4937 - 4946 ［ DOI： 10.1109/CVPR42600.2020.00499 http://dx.doi.org/10.1109/CVPR42600.2020.00499 ］
Sarlin P E ， Dusmanu M ， Schönberger J L ， Speciale P ， Gruber L ， Larsson V ， Miksik O and Pollefeys M . 2022 . LaMAR： benchmarking localization and mapping for augmented reality // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 686 - 704 ［ DOI： 10.1007/978-3-031-20071-7_40 http://dx.doi.org/10.1007/978-3-031-20071-7_40 ］
Sattler T ， Havlena M ， Radenovic F ， Schindler K and Pollefeys M . 2015 . Hyperpoints and fine vocabularies for large-scale location recognition // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 2102 - 2110 ［ DOI： 10.1109/ICCV.2015.243 http://dx.doi.org/10.1109/ICCV.2015.243 ］
Sattler T ， Leibe B and Kobbelt L . 2011 . Fast image-based localization using direct 2D-to-3D matching // Proceedings of 2011 IEEE International Conference on Computer Vision . Barcelona， Spain ： IEEE： 667 - 674 ［ DOI： 10.1109/ICCV.2011.6126302 http://dx.doi.org/10.1109/ICCV.2011.6126302 ］
Sattler T ， Leibe B and Kobbelt L . 2012 . Improving image-based localization by active correspondence search // Proceedings of the 12th European Conference on Computer Vision . Florence， Italy ： Springer： 752 - 765 ［ DOI： 10.1007/978-3-642-33718-5_54 http://dx.doi.org/10.1007/978-3-642-33718-5_54 ］
Sattler T ， Leibe B and Kobbelt L . 2017 . Efficient and effective prioritized matching for large-scale image-based localization . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 39 （ 9 ）： 1744 - 1756 ［ DOI： 10.1109/TPAMI.2016.2611662 http://dx.doi.org/10.1109/TPAMI.2016.2611662 ］
Sattler T ， Maddern W ， Toft C ， Torii A ， Hammarstrand L ， Stenborg E ， Safari D ， Okutomi M ， Pollefeys M ， Sivic J ， Kahl F and Pajdla T . 2018 . Benchmarking 6DOF outdoor visual localization in changing conditions // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 8601 - 8610 ［ DOI： 10.1109/CVPR.2018.00897 http://dx.doi.org/10.1109/CVPR.2018.00897 ］
Sattler T ， Zhou Q J ， Pollefeys M and Leal-Taixé L . 2019 . Understanding the limitations of CNN-based absolute camera pose regression // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 3297 - 3307 ［ DOI： 10.1109/CVPR.2019.00342 http://dx.doi.org/10.1109/CVPR.2019.00342 ］
Schonberger J L and Frahm J M . 2016 . Structure-from-motion revisited // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 4104 - 4113 ［ DOI： 10.1109/CVPR.2016.445 http://dx.doi.org/10.1109/CVPR.2016.445 ］
Schreiber M ， Knöppel C and Franke U . 2013 . LaneLoc： lane marking based localization using highly accurate maps // Proceedings of 2013 IEEE Intelligent Vehicles Symposium . Gold Coast， Australia ： IEEE： 449 - 454 ［ DOI： 10.1109/IVS.2013.6629509 http://dx.doi.org/10.1109/IVS.2013.6629509 ］
Shavit Y ， Ferens R and Keller Y . 2021 . Learning multi-scene absolute pose regression with Transformers // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 2713 - 2722 ［ DOI： 10.1109/ICCV48922.2021.00273 http://dx.doi.org/10.1109/ICCV48922.2021.00273 ］
Shi T X ， Shen S H ， Gao X and Zhu L J . 2019 . Visual localization using sparse semantic 3D map // Proceedings of 2019 IEEE International Conference on Image Processing . Taipei， China ： IEEE： 315 - 319 ［ DOI： 10.1109/ICIP.2019.8802957 http://dx.doi.org/10.1109/ICIP.2019.8802957 ］
Shi Y ， Cai J X ， Shavit Y ， Mu T J ， Feng W S and Zhang K . 2022 . ClusterGNN： cluster-based coarse-to-fine graph neural network for efficient feature matching // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 12507 - 12516 ［ DOI： 10.1109/CVPR52688.2022.01219 http://dx.doi.org/10.1109/CVPR52688.2022.01219 ］
Shotton J ， Glocker B ， Zach C ， Izadi S ， Criminisi A and Fitzgibbon A . 2013 . Scene coordinate regression forests for camera relocalization in RGB-D images // Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition . Portland， USA ： IEEE： 2930 - 2937 ［ DOI： 10.1109/CVPR.2013.377 http://dx.doi.org/10.1109/CVPR.2013.377 ］
Speciale P ， Schonberger J L ， Kang S B ， Sinha S N and Pollefeys M . 2019 . Privacy preserving image-based localization // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 5488 - 5498 ［ DOI： 10.1109/CVPR.2019.00564 http://dx.doi.org/10.1109/CVPR.2019.00564 ］
Stewénius H ， Engels C and Nistér D . 2006 . Recent developments on direct relative orientation . ISPRS Journal of Photogrammetry and Remote Sensing ， 60 （ 4 ）： 284 - 294 ［ DOI： 10.1016/j.isprsjprs.2006.03.005 http://dx.doi.org/10.1016/j.isprsjprs.2006.03.005 ］
Sucar E ， Liu S K ， Ortiz J and Davison A J . 2021 . IMAP： implicit mapping and positioning in real-time // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 6209 - 6218 ［ DOI： 10.1109/ICCV48922.2021.00617 http://dx.doi.org/10.1109/ICCV48922.2021.00617 ］
Tang S T ， Tang C Z ， Huang R ， Zhu S Y and Tan P . 2021 . Learning camera localization via dense scene matching // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 1831 - 1841 ［ DOI： 10.1109/CVPR46437.2021.00187 http://dx.doi.org/10.1109/CVPR46437.2021.00187 ］
Tang S T ， Tang S C ， Tagliasacchi A ， Tan P and Furukawa Y . 2023 . NeuMap： neural coordinate mapping by auto-transdecoder for camera localization // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： IEEE： 929 - 939 ［ DOI： 10.1109/CVPR52729.2023.00096 http://dx.doi.org/10.1109/CVPR52729.2023.00096 ］
Toft C ， Maddern W ， Torii A ， Hammarstrand L ， Stenborg E ， Safari D ， Okutomi M ， Pollefeys M ， Sivic J ， Pajdla T ， Kahl F and Sattler T . 2022 . Long-term visual localization revisited . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 44 （ 4 ）： 2074 - 2088 ［ DOI： 10.1109/TPAMI.2020.3032010 http://dx.doi.org/10.1109/TPAMI.2020.3032010 ］
Torii A ， Arandjelović R ， Sivic J ， Okutomi M and Pajdla T . 2015 . 24/7 place recognition by view synthesis // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 1808 - 1817 ［ DOI： 10.1109/CVPR.2015.7298790 http://dx.doi.org/10.1109/CVPR.2015.7298790 ］
Truong P ， Rakotosaona M J ， Manhardt F and Tombari F . 2023 . SPARF： neural radiance fields from sparse and noisy poses // Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver， Canada ： 4190 - 4200 ［ DOI： 10.1109/CVPR52729.2023.00408 http://dx.doi.org/10.1109/CVPR52729.2023.00408 ］
Valada A ， Radwan N and Burgard W . 2018 . Deep auxiliary learning for visual localization and odometry // Proceedings of 2018 IEEE International Conference on Robotics and Automation . Brisbane， Australia ： IEEE： 6939 - 6946 ［ DOI： 10.1109/ICRA.2018.8462979 http://dx.doi.org/10.1109/ICRA.2018.8462979 ］
Valentin J ， Dai A ， Niessner M ， Kohli P ， Torr P ， Izadi S and Keskin C . 2016 . Learning to navigate the energy landscape // Proceedings of the 4th International Conference on 3D Vision （3DV） . Stanford， USA ： 323 - 332 ［ DOI： 10.1109/3DV.2016.41 http://dx.doi.org/10.1109/3DV.2016.41 ］
Valentin J ， Niebner M ， Shotton J ， Fitzgibbon A ， Izadi S and Torr P . 2015 . Exploiting uncertainty in regression forests for accurate camera relocalization // Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition . Boston， USA ： IEEE： 4400 - 4408 ［ DOI： 10.1109/CVPR.2015.7299069 http://dx.doi.org/10.1109/CVPR.2015.7299069 ］
Walch F ， Hazirbas C ， Leal-Taixé L ， Sattler T ， Hilsenbeck S and Cremers D . 2017 . Image-based localization using LSTMs for structured feature correlation // Proceedings of 2017 IEEE International Conference on Computer Vision . Venice， Italy ： IEEE： 627 - 637 ［ DOI： 10.1109/ICCV.2017.75 http://dx.doi.org/10.1109/ICCV.2017.75 ］
Wang B ， Chen C H ， Lu C X ， Zhao P J ， Trigoni N and Markham A . 2020 . AtLoc： attention guided camera localization // Proceedings of the 34th AAAI Conference on Artificial Intelligence . New York， USA ： AAAI Press： 10393 - 10401 ［ DOI： 10.1609/aaai.v34i06.6608 http://dx.doi.org/10.1609/aaai.v34i06.6608 ］
Wang Z R ， Wu S Z ， Xie W D ， Chen M and Prisacariu V . 2021 . NeRF--： neural radiance fields without known camera parameters ［EB/OL］. ［ 2023-09-09 ］. https://arxiv.org/pdf/2102.07064.pdf https://arxiv.org/pdf/2102.07064.pdf
Wei D ， Wan Y ， Zhang Y J ， Liu X Y ， Zhang B and Wang X Q . 2022 . ELSR： efficient line segment reconstruction with planes and points guidance // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 15786 - 15794 ［ DOI： 10.1109/CVPR52688.2022.01535 http://dx.doi.org/10.1109/CVPR52688.2022.01535 ］
Wen T P ， Jiang K ， Wijaya B ， Li H Y ， Yang M M and Yang D G . 2022 . TM3Loc： tightly-coupled monocular map matching for high precision vehicle localization . IEEE Transactions on Intelligent Transportation Systems ， 23 （ 11 ）： 20268 - 20281 ［ DOI： 10.1109/TITS.2022.3176914 http://dx.doi.org/10.1109/TITS.2022.3176914 ］
Wu J ， Ma L W and Hu X L . 2017 . Delving deeper into convolutional neural networks for camera relocalization // Proceedings of 2017 IEEE International Conference on Robotics and Automation . Singapore，Singapore ： IEEE： 5644 - 5651 ［ DOI： 10.1109/ICRA.2017.7989663 http://dx.doi.org/10.1109/ICRA.2017.7989663 ］
Wu T and Ranganathan A . 2013 . Vehicle localization using road markings // Proceedings of 2013 IEEE Intelligent Vehicles Symposium . Gold Coast， Australia ： IEEE： 1185 - 1190 ［ DOI： 10.1109/IVS.2013.6629627 http://dx.doi.org/10.1109/IVS.2013.6629627 ］
Xu C ， Zhang L L ， Cheng L and Koch R . 2017 . Pose estimation from line correspondences： a complete analysis and a series of solutions . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 39 （ 6 ）： 1209 - 1222 ［ DOI： 10.1109/TPAMI.2016.2582162 http://dx.doi.org/10.1109/TPAMI.2016.2582162 ］
Xue F ， Wang X ， Yan Z K ， Wang Q Y ， Wang J Q and Zha H B . 2019 . Local supports global： deep camera relocalization with sequence enhancement // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 2841 - 2850 ［ DOI： 10.1109/ICCV.2019.00293 http://dx.doi.org/10.1109/ICCV.2019.00293 ］
Yang L W ， Bai Z Q ， Tang C Z ， Li H H ， Furukawa Y and Tan P . 2019 . SANet： scene agnostic network for camera localization // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 42 - 51 ［ DOI： 10.1109/ICCV.2019.00013 http://dx.doi.org/10.1109/ICCV.2019.00013 ］
Yang L W ， Shrestha R ， Li W B ， Liu S C ， Zhang G F ， Cui Z P and Tan P . 2022 . SceneSqueezer： learning to compress scene for camera relocalization // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 8249 - 8258 ［ DOI： 10.1109/CVPR52688.2022.00808 http://dx.doi.org/10.1109/CVPR52688.2022.00808 ］
Yen-Chen L ， Florence P ， Barron J T ， Rodriguez A ， Isola P and Lin T Y . 2021 . INeRF： inverting neural radiance fields for pose estimation // Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems . Prague， Czech Republic ： IEEE： 1323 - 1330 ［ DOI： 10.1109/IROS51168.2021.9636708 http://dx.doi.org/10.1109/IROS51168.2021.9636708 ］
Yoon S and Kim A . 2021 . Line as a visual sentence： context-aware line descriptor for visual localization . IEEE Robotics and Automation Letters ， 6 （ 4 ）： 8726 - 8733 ［ DOI： 10.1109/LRA.2021.3111760 http://dx.doi.org/10.1109/LRA.2021.3111760 ］
Yu H ， Zhen W K ， Yang W ， Zhang J and Scherer S . 2020 . Monocular camera localization in prior LiDAR maps with 2D-3D line correspondences // Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems . Las Vegas， USA ： IEEE： 4588 - 4594 ［ DOI： 10.1109/IROS45743.2020.9341690 http://dx.doi.org/10.1109/IROS45743.2020.9341690 ］
Zhang C ， Liu H ， Xie Z J ， Yang K Y ， Guo K ， Cai R and Li Z W . 2021 . AVP-Loc： surround view localization and relocalization based on HD vector map for automated valet parking // Proceedings of 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems . Prague， Czech Republic ： IEEE： 5552 - 5559 ［ DOI： 10.1109/IROS51168.2021.9636746 http://dx.doi.org/10.1109/IROS51168.2021.9636746 ］
Zhou L P ， Ye J M and Kaess M . 2019a . A stable algebraic camera pose estimation for minimal configurations of 2D/3D point and line correspondences // Proceedings of the 14th Asian Conference on Computer Vision . Perth， Australia ： Springer： 273 - 288 ［ DOI： 10.1007/978-3-030-20870-7_17 http://dx.doi.org/10.1007/978-3-030-20870-7_17 ］
Zhou Q J ， Agostinho S ， Ošep A and Leal-Taixé L . 2022 . Is geometry enough for matching in visual localization? // Proceedings of the 17th European Conference on Computer Vision . Tel Aviv， Israel ： Springer： 407 - 425 ［ DOI： 10.1007/978-3-031-20080-9_24 http://dx.doi.org/10.1007/978-3-031-20080-9_24 ］
Zhou Q J ， Sattler T ， Pollefeys M and Leal-Taixé L . 2020 . To learn or not to learn： visual localization from essential matrices // Proceedings of 2020 IEEE International Conference on Robotics and Automation . Paris， France ： IEEE： 3319 - 3326 ［ DOI： 10.1109/ICRA40945.2020.9196607 http://dx.doi.org/10.1109/ICRA40945.2020.9196607 ］
Zhou T H ， Brown M ， Snavely N and Lowe D G . 2017 . Unsupervised learning of depth and ego-motion from video // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 6612 - 6619 ［ DOI： 10.1109/CVPR.2017.700 http://dx.doi.org/10.1109/CVPR.2017.700 ］
Zhou Y ， Barnes C ， Lu J W ， Yang J M and Li H . 2019b . On the continuity of rotation representations in neural networks // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 5738 - 5746 ［ DOI： 10.1109/CVPR.2019.00589 http://dx.doi.org/10.1109/CVPR.2019.00589 ］
Zhu Z H ， Peng S Y ， Larsson V ， Xu W W ， Bao H J ， Cui Z P ， Oswald M R and Pollefeys M . 2022 . NICE-SLAM： neural implicit scalable encoding for SLAM // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 12776 - 12786 ［ DOI： 10.1109/CVPR52688.2022.01245 http://dx.doi.org/10.1109/CVPR52688.2022.01245 ］
Zhu Z X ， Chen Y T ， Wu Z R ， Hou C ， Shi Y L ， Li C X ， Li P F ， Zhao H and Zhou G Y . 2023 . LATITUDE： robotic global localization with truncated dynamic low-pass filter in city-scale NeRF // Proceedings of 2023 IEEE International Conference on Robotics and Automation . London， United Kingdom ： IEEE： 8326 - 8332 ［ DOI： 10.1109/ICRA48891.2023.10161570 http://dx.doi.org/10.1109/ICRA48891.2023.10161570 ］
相关作者
相关机构
京公网安备11010802024621
