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MeshLoc: Mesh-Based Visual Localization

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13682))

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Abstract

Visual localization, i.e., the problem of camera pose estimation, is a central component of applications such as autonomous robots and augmented reality systems. A dominant approach in the literature, shown to scale to large scenes and to handle complex illumination and seasonal changes, is based on local features extracted from images. The scene representation is a sparse Structure-from-Motion point cloud that is tied to a specific local feature. Switching to another feature type requires an expensive feature matching step between the database images used to construct the point cloud. In this work, we thus explore a more flexible alternative based on dense 3D meshes that does not require features matching between database images to build the scene representation. We show that this approach can achieve state-of-the-art results. We further show that surprisingly competitive results can be obtained when extracting features on renderings of these meshes, without any neural rendering stage, and even when rendering raw scene geometry without color or texture. Our results show that dense 3D model-based representations are a promising alternative to existing representations and point to interesting and challenging directions for future research.

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Notes

  1. 1.

    We actually optimize a robust MSAC-like cost function [37] not the number of inliers.

References

  1. Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building Rome in a day. In: ICCV 2009, pp. 72–79 (2009)

    Google Scholar 

  2. Arandjelović, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: CVPR (2016)

    Google Scholar 

  3. Balntas, V., Li, S., Prisacariu, V.: RelocNet: continuous metric learning relocalisation using neural nets. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 782–799. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_46

    Chapter  Google Scholar 

  4. Barath, D., Ivashechkin, M., Matas, J.: Progressive NAPSAC: sampling from gradually growing neighborhoods. arXiv preprint arXiv:1906.02295 (2019)

  5. Barath, D., Matas, J.: Graph-cut RANSAC. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6733–6741 (2018)

    Google Scholar 

  6. Barath, D., Noskova, J., Ivashechkin, M., Matas, J.: MAGSAC++, a fast, reliable and accurate robust estimator. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1304–1312 (2020)

    Google Scholar 

  7. Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5835–5844 (2021)

    Google Scholar 

  8. Brachmann, E., Humenberger, M., Rother, C., Sattler, T.: On the limits of pseudo ground truth in visual camera re-localisation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6218–6228 (2021)

    Google Scholar 

  9. Brachmann, E., Krull, A., Nowozin, S., Shotton, J., Michel, F., Gumhold, S., Rother, C.: DSAC - differentiable RANSAC for camera localization. In: CVPR (2017)

    Google Scholar 

  10. Brachmann, E., Rother, C.: Learning less is more - 6D camera localization via 3D surface regression. In: CVPR (2018)

    Google Scholar 

  11. Brachmann, E., Rother, C.: Expert sample consensus applied to camera re-localization. In: ICCV (2019)

    Google Scholar 

  12. Brachmann, E., Rother, C.: Visual camera re-localization from RGB and RGB-D images using DSAC. TPAMI 44, 5847–5865 (2021)

    Google Scholar 

  13. Brejcha, J., Lukáč, M., Hold-Geoffroy, Y., Wang, O., Čadík, M.: LandscapeAR: large scale outdoor augmented reality by matching photographs with terrain models using learned descriptors. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 295–312. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58526-6_18

    Chapter  Google Scholar 

  14. Cavallari, T., Bertinetto, L., Mukhoti, J., Torr, P., Golodetz, S.: Let’s take this online: adapting scene coordinate regression network predictions for online RGB-D camera relocalisation. In: 3DV (2019)

    Google Scholar 

  15. Cavallari, T., Golodetz, S., Lord, N.A., Valentin, J., Di Stefano, L., Torr, P.H.S.: On-the-fly adaptation of regression forests for online camera relocalisation. In: CVPR (2017)

    Google Scholar 

  16. Cavallari, T., et al.: Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade. TPAMI 42, 2465–2477 (2019)

    Article  Google Scholar 

  17. Chum, O., Matas, J.: Randomized RANSAC with \({T}_{d, d}\) test. In: British Machine Vision Conference (BMVC) (2002)

    Google Scholar 

  18. Chum, O., Perdoch, M., Matas, J.: Geometric min-hashing: finding a (thick) needle in a haystack. In: ICCV (2007)

    Google Scholar 

  19. Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference (2008)

    Google Scholar 

  20. Dai, A., Nießner, M., Zollöfer, M., Izadi, S., Theobalt, C.: BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration. TOG 36, 1 (2017)

    Article  Google Scholar 

  21. DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: CVPR Workshops (2018)

    Google Scholar 

  22. Ding, M., Wang, Z., Sun, J., Shi, J., Luo, P.: CamNet: coarse-to-fine retrieval for camera re-localization. In: ICCV (2019)

    Google Scholar 

  23. Dusmanu, M., et al.: D2-Net: a trainable CNN for joint detection and description of local features. In: CVPR (2019)

    Google Scholar 

  24. Fischler, M.A., Bolles, R.C.: Random sampling consensus: a paradigm for model fitting with application to image analysis and automated cartography. CACM (1981)

    Google Scholar 

  25. Gordo, A., Almazan, J., Revaud, J., Larlus, D.: End-to-end learning of deep visual representations for image retrieval. Int. J. Comput. Vision 124(2), 237–254 (2017)

    Article  MathSciNet  Google Scholar 

  26. Guzov, V., Mir, A., Sattler, T., Pons-Moll, G.: Human POSEitioning system (HPS): 3D human pose estimation and self-localization in large scenes from body-mounted sensors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4318–4329 (2021)

    Google Scholar 

  27. Heng, L., et al.: Project AutoVision: localization and 3D scene perception for an autonomous vehicle with a multi-camera system. In: ICRA (2019)

    Google Scholar 

  28. Humenberger, M., et al.: Robust image retrieval-based visual localization using kapture. arXiv:2007.13867 (2020)

  29. Irschara, A., Zach, C., Frahm, J.M., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: CVPR (2009)

    Google Scholar 

  30. Jafarzadeh, A., et al.: CrowdDriven: a new challenging dataset for outdoor visual localization. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9825–9835 (2021)

    Google Scholar 

  31. Jakob, W., Tarini, M., Panozzo, D., Sorkine-Hornung, O.: Instant field-aligned meshes. ACM Trans. Graph. 34(6), 189–1 (2015)

    Article  Google Scholar 

  32. Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32(3) (2013)

    Google Scholar 

  33. Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: CVPR (2017)

    Google Scholar 

  34. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DOF camera relocalization. In: ICCV (2015)

    Google Scholar 

  35. Larsson, V.: PoseLib - minimal solvers for camera pose estimation (2020). https://github.com/vlarsson/PoseLib

  36. Laskar, Z., Melekhov, I., Kalia, S., Kannala, J.: Camera relocalization by computing pairwise relative poses using convolutional neural network. In: ICCV Workshops (2017)

    Google Scholar 

  37. Lebeda, K., Matas, J.E.S., Chum, O.: Fixing the locally optimized RANSAC. In: BMVC (2012)

    Google Scholar 

  38. Li, Y., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3D point clouds. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 15–29. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_2

    Chapter  Google Scholar 

  39. Li, Y., Snavely, N., Huttenlocher, D.P.: Location recognition using prioritized feature matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15552-9_57

    Chapter  Google Scholar 

  40. Lim, H., Sinha, S.N., Cohen, M.F., Uyttendaele, M.: Real-time image-based 6-DOF localization in large-scale environments. In: CVPR (2012)

    Google Scholar 

  41. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)

    Article  Google Scholar 

  42. Lynen, S., Sattler, T., Bosse, M., Hesch, J., Pollefeys, M., Siegwart, R.: Get out of my lab: large-scale, real-time visual-inertial localization. In: RSS (2015)

    Google Scholar 

  43. Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., Duckworth, D.: NeRF in the wild: neural radiance fields for unconstrained photo collections. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7206–7215 (2021)

    Google Scholar 

  44. Massiceti, D., Krull, A., Brachmann, E., Rother, C., Torr, P.H.: Random forests versus neural networks - what’s best for camera relocalization? In: ICRA (2017)

    Google Scholar 

  45. Middelberg, S., Sattler, T., Untzelmann, O., Kobbelt, L.: Scalable 6-DOF localization on mobile devices. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 268–283. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_18

    Chapter  Google Scholar 

  46. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24

    Chapter  Google Scholar 

  47. Moreau, A., Piasco, N., Tsishkou, D., Stanciulescu, B., de La Fortelle, A.: LENS: localization enhanced by neRF synthesis. In: CoRL (2021)

    Google Scholar 

  48. Mueller, M.S., Sattler, T., Pollefeys, M., Jutzi, B.: Image-to-image translation for enhanced feature matching, image retrieval and visual localization. ISPRS Ann. Photogram. Remote Sens. Spatial Inf. Sci. (2019)

    Google Scholar 

  49. Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41(4), 102:1–102:15 (2022). https://doi.org/10.1145/3528223.3530127

  50. Naseer, T., Burgard, W.: Deep regression for monocular camera-based 6-DoF global localization in outdoor environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)

    Google Scholar 

  51. Ng, T., Rodriguez, A.L., Balntas, V., Mikolajczyk, K.: Reassessing the limitations of CNN methods for camera pose regression. CoRR abs/2108.07260 (2021)

    Google Scholar 

  52. Oechsle, M., Peng, S., Geiger, A.: UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021)

    Google Scholar 

  53. Persson, M., Nordberg, K.: Lambda twist: an accurate fast robust perspective three point (P3P) solver. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 334–349. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_20

    Chapter  Google Scholar 

  54. Pittaluga, F., Koppal, S.J., Kang, S.B., Sinha, S.N.: Revealing scenes by inverting structure from motion reconstructions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  55. Revaud, J., Almazán, J., Rezende, R.S., Souza, C.R.D.: Learning with average precision: training image retrieval with a listwise loss. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5107–5116 (2019)

    Google Scholar 

  56. Revaud, J., Weinzaepfel, P., de Souza, C.R., Humenberger, M.: R2D2: repeatable and reliable detector and descriptor. In: NeurIPS (2019)

    Google Scholar 

  57. Rocco, I., Arandjelović, R., Sivic, J.: Efficient neighbourhood consensus networks via submanifold sparse convolutions. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 605–621. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_35

    Chapter  Google Scholar 

  58. Rocco, I., Cimpoi, M., Arandjelović, R., Torii, A., Pajdla, T., Sivic, J.: Neighbourhood consensus networks. Adv. Neural Inf. Process. Syst. 31 (2018)

    Google Scholar 

  59. Sarlin, P.E., Cadena, C., Siegwart, R., Dymczyk, M.: From coarse to fine: robust hierarchical localization at large scale. In: CVPR (2019)

    Google Scholar 

  60. Sarlin, P.E., Debraine, F., Dymczyk, M., Siegwart, R., Cadena, C.: Leveraging deep visual descriptors for hierarchical efficient localization. In: Conference on Robot Learning (CoRL) (2018)

    Google Scholar 

  61. Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperGlue: learning feature matching with graph neural networks. In: CVPR (2020)

    Google Scholar 

  62. Sarlin, P.E., et al.: Back to the feature: learning robust camera localization from pixels to pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3247–3257 (2021)

    Google Scholar 

  63. Sattler, T., Havlena, M., Radenovic, F., Schindler, K., Pollefeys, M.: Hyperpoints and fine vocabularies for large-scale location recognition. In: ICCV (2015)

    Google Scholar 

  64. Sattler, T., Leibe, B., Kobbelt, L.: Improving image-based localization by active correspondence search. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 752–765. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_54

    Chapter  Google Scholar 

  65. Sattler, T., Leibe, B., Kobbelt, L.: Efficient & effective prioritized matching for large-scale image-based localization. PAMI 39, 1744–1756 (2017)

    Article  Google Scholar 

  66. Sattler, T., et al.: Benchmarking 6DOF urban visual localization in changing conditions. In: CVPR (2018)

    Google Scholar 

  67. Sattler, T., Weyand, T., Leibe, B., Kobbelt, L.: Image retrieval for image-based localization revisited. In: BMVC (2012)

    Google Scholar 

  68. Sattler, T., Zhou, Q., Pollefeys, M., Leal-Taixé, L.: Understanding the limitations of cnn-based absolute camera pose regression. In: CVPR (2019)

    Google Scholar 

  69. Schönberger, J.L., Pollefeys, M., Geiger, A., Sattler, T.: Semantic visual localization. In: CVPR (2018)

    Google Scholar 

  70. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)

    Google Scholar 

  71. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  72. Shan, Q., Wu, C., Curless, B., Furukawa, Y., Hernandez, C., Seitz, S.M.: Accurate geo-registration by ground-to-aerial image matching. In: 3DV (2014)

    Google Scholar 

  73. Shavit, Y., Ferens, R., Keller, Y.: Learning multi-scene absolute pose regression with transformers. In: ICCV (2021)

    Google Scholar 

  74. Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in RGB-D images. In: CVPR (2013)

    Google Scholar 

  75. Sibbing, D., Sattler, T., Leibe, B., Kobbelt, L.: SIFT-realistic rendering. In: 3DV (2013)

    Google Scholar 

  76. Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. IJCV 80, 189–210 (2008)

    Article  Google Scholar 

  77. Song, Z., Chen, W., Campbell, D., Li, H.: Deep novel view synthesis from colored 3D point clouds. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12369, pp. 1–17. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58586-0_1

    Chapter  Google Scholar 

  78. Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: Loftr: detector-free local feature matching with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  79. Svärm, L., Enqvist, O., Kahl, F., Oskarsson, M.: City-scale localization for cameras with known vertical direction. PAMI 39(7), 1455–1461 (2017)

    Article  Google Scholar 

  80. Taira, H., et al.: InLoc: indoor visual localization with dense matching and view synthesis. In: CVPR (2018)

    Google Scholar 

  81. Taira, H., et al.: Is this the right place? geometric-semantic pose verification for indoor visual localization. In: The IEEE International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  82. Tancik, M., et al.: Block-NeRF: scalable large scene neural view synthesis. ArXiv abs/2202.05263 (2022)

    Google Scholar 

  83. Toft, C., et al.: Long-term visual localization revisited. TPAMI 1 (2020). https://doi.org/10.1109/TPAMI.2020.3032010

  84. Tomešek, J., Čadík, M., Brejcha, J.: CrossLocate: cross-modal large-scale visual geo-localization in natural environments using rendered modalities. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2193–2202 (2022)

    Google Scholar 

  85. Torii, A., Arandjelović, R., Sivic, J., Okutomi, M., Pajdla, T.: 24/7 place recognition by view synthesis. In: CVPR (2015)

    Google Scholar 

  86. Valentin, J., et al.: Learning to navigate the energy landscape. In: 3DV (2016)

    Google Scholar 

  87. Waechter, M., Beljan, M., Fuhrmann, S., Moehrle, N., Kopf, J., Goesele, M.: Virtual rephotography: novel view prediction error for 3D Reconstruction. ACM Trans. Graph. 36(1) (2017)

    Google Scholar 

  88. Waechter, M., Moehrle, N., Goesele, M.: Let there be color! large-scale texturing of 3D reconstructions. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 836–850. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_54

    Chapter  Google Scholar 

  89. Walch, F., Hazirbas, C., Leal-Taixé, L., Sattler, T., Hilsenbeck, S., Cremers, D.: Image-based localization using LSTMs for structured feature correlation. In: ICCV (2017)

    Google Scholar 

  90. Wang, Q., et al.: IBRNet: learning multi-view image-based rendering. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4688–4697 (2021)

    Google Scholar 

  91. Wang, Q., Zhou, X., Hariharan, B., Snavely, N.: Learning feature descriptors using camera pose supervision. arXiv:2004.13324 (2020)

  92. Zeisl, B., Sattler, T., Pollefeys, M.: Camera pose voting for large-scale image-based localization. In: ICCV (2015)

    Google Scholar 

  93. Zhang, Z., Sattler, T., Scaramuzza, D.: Reference pose generation for long-term visual localization via learned features and view synthesis. IJCV 129, 821–844 (2020)

    Article  Google Scholar 

  94. Zhou, Q., Sattler, T., Leal-Taixe, L.: Patch2pix: epipolar-guided pixel-level correspondences. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

    Google Scholar 

  95. Zhou, Q., Sattler, T., Pollefeys, M., Leal-Taixé, L.: To learn or not to learn: visual localization from essential matrices. In: ICRA (2019)

    Google Scholar 

  96. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)

    Google Scholar 

  97. Zhukov, S., Iones, A., Kronin, G.: An ambient light illumination model. In: Rendering Techniques (1998)

    Google Scholar 

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Acknowledgement

This work was supported by the EU Horizon 2020 project RICAIP (grant agreement No. 857306), the European Regional Development Fund under project IMPACT (No. CZ.02.1.01/0.0/0.0/15_003/0000468), a Meta Reality Labs research award under project call ’Benchmarking City-Scale 3D Map Making with Mapillary Metropolis’, the Grant Agency of the Czech Technical University in Prague (No. SGS21/119/OHK3/2T/13), the OP VVV funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”, and the ERC-CZ grant MSMT LL1901.

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  1. Faculty of Electrical Engineering, Czech Technical University (CTU) in Prague, Prague, Czech Republic

    Vojtech Panek

  2. Czech Institute of Informatics, Robotics and Cybernetics CTU in Prague, Prague, Czech Republic

    Vojtech Panek & Torsten Sattler

  3. Visual Recognition Group, Faculty of Electrical Engineering CTU in Prague, Prague, Czech Republic

    Zuzana Kukelova

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  1. Vojtech Panek
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Correspondence to Vojtech Panek .

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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Panek, V., Kukelova, Z., Sattler, T. (2022). MeshLoc: Mesh-Based Visual Localization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13682. Springer, Cham. https://doi.org/10.1007/978-3-031-20047-2_34

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