Skip to main content
Springer Nature Link
Log in

Robust Incremental Structure-from-Motion with Hybrid Features

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15094))

Included in the following conference series:

  • 450 Accesses

Abstract

Structure-from-Motion (SfM) has become a ubiquitous tool for camera calibration and scene reconstruction with many downstream applications in computer vision and beyond. While the state-of-the-art SfM pipelines have reached a high level of maturity in well-textured and well-configured scenes over the last decades, they still fall short of robustly solving the SfM problem in challenging scenarios. In particular, weakly textured scenes and poorly constrained configurations oftentimes cause catastrophic failures or large errors for the primarily keypoint-based pipelines. In these scenarios, line segments are often abundant and can offer complementary geometric constraints. Their large spatial extent and typically structured configurations lead to stronger geometric constraints as compared to traditional keypoint-based methods. In this work, we introduce an incremental SfM system that, in addition to points, leverages lines and their structured geometric relations. Our technical contributions span the entire pipeline (mapping, triangulation, registration) and we integrate these into a comprehensive end-to-end SfM system that we share as an open-source software with the community. We also present the first analytical method to propagate uncertainties for 3D optimized lines via sensitivity analysis. Experiments show that our system is consistently more robust and accurate compared to the widely used point-based state of the art in SfM – achieving richer maps and more precise camera registrations, especially under challenging conditions. In addition, our uncertainty-aware localization module alone is able to consistently improve over the state of the art under both point-alone and hybrid setups.

S. Liu, Y. Gao and T. Zhang—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdellali, H., Frohlich, R., Vilagos, V., Kato, Z.: L2d2: learnable line detector and descriptor. In: International Conference on 3D Vision (3DV) (2021)

    Google Scholar 

  2. Agarwal, S., et al.: Building Rome in a day. Commun. ACM 54(10), 105–112 (2011)

    Article  Google Scholar 

  3. Agarwal, S., Mierle, K.: Ceres solver. http://ceres-solver.org

  4. Agarwal, S., Snavely, N., Seitz, S.M., Szeliski, R.: Bundle adjustment in the large. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 29–42. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15552-9_3

    Chapter  Google Scholar 

  5. Åström, K., Kahl, F., Heyden, A., Berthilsson, R.: A statistical approach to structure and motion from image features. In: Advances in Pattern Recognition: Joint IAPR International Workshops SSPR 1998 and SPR 1998 (1998)

    Google Scholar 

  6. Bartoli, A., Coquerelle, M., Sturm, P.: A framework for pencil-of-points structure-from-motion. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3022, pp. 28–40. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24671-8_3

    Chapter  Google Scholar 

  7. Bartoli, A., Sturm, P.: Structure-from-motion using lines: representation, triangulation, and bundle adjustment. Computer Vis, Image Understand. (CVIU) 100(3), 416–441 (2005)

    Article  Google Scholar 

  8. Bazin, J.C., et al.: Globally optimal line clustering and vanishing point estimation in manhattan world. In: CVPR (2012)

    Google Scholar 

  9. Bhowmick, B., Patra, S., Chatterjee, A., Govindu, V.M., Banerjee, S.: Divide and conquer: Efficient large-scale structure from motion using graph partitioning. In: ACCV (2015)

    Google Scholar 

  10. Brooks, M.J., Chojnacki, W., Gawley, D., Van Den Hengel, A.: What value covariance information in estimating vision parameters? In: ICCV (2001)

    Google Scholar 

  11. Bui, B.T., Bui, H.H., Tran, D.T., Lee, J.H.: Representing 3d sparse map points and lines for camera relocalization. arXiv preprint arXiv:2402.18011 (2024)

  12. Burnett, K., Yoon, D.J., Schoellig, A.P., Barfoot, T.D.: Radar odometry combining probabilistic estimation and unsupervised feature learning. In: Robotics: Science and Systems (RSS) (2021)

    Google Scholar 

  13. Camposeco, F., Cohen, A., Pollefeys, M., Sattler, T.: Hybrid camera pose estimation. In: CVPR (2018)

    Google Scholar 

  14. Chandraker, M., Lim, J., Kriegman, D.: Moving in stereo: efficient structure and motion using lines. In: ICCV (2009)

    Google Scholar 

  15. Chum, O., Matas, J., Kittler, J.: Locally optimized ransac. In: Joint Pattern Recognition Symposium (2003)

    Google Scholar 

  16. Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.: Discrete-continuous optimization for large-scale structure from motion. In: CVPR (2011)

    Google Scholar 

  17. Dellaert, F., Seitz, S.M., Thorpe, C.E., Thrun, S.: Structure from motion without correspondence. In: CVPR (2000)

    Google Scholar 

  18. DeTone, D., Malisiewicz, T., Rabinovich, A.: Superpoint: Self-supervised interest point detection and description. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2018)

    Google Scholar 

  19. Dusmanu, M., Miksik, O., Schönberger, J.L., Pollefeys, M.: Cross-Descriptor Visual Localization and Mapping. In: ICCV (2021)

    Google Scholar 

  20. Dusmanu, M., Schönberger, J.L., Pollefeys, M.: Multi-view optimization of local feature geometry. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 670–686. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_39

    Chapter  Google Scholar 

  21. Fiacco, A.V., Ishizuka, Y.: Sensitivity and stability analysis for nonlinear programming. Ann. Oper. Res. 27(1), 215–235 (1990)

    Article  MathSciNet  Google Scholar 

  22. Förstner, W., Gülch, E.: A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: Proc. ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data (1987)

    Google Scholar 

  23. Förstner, W., Wrobel, B.P.: Photogrammetric computer vision (2016)

    Google Scholar 

  24. Frahm, J.-M., et al.: Building Rome on a cloudless day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_27

    Chapter  Google Scholar 

  25. Gao, X.S., Hou, X.R., Tang, J., Cheng, H.F.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 25(8), 930–943 (2003)

    Google Scholar 

  26. Germain, H., Bourmaud, G., Lepetit, V.: S2dnet: Learning accurate correspondences for sparse-to-dense feature matching. In: ECCV (2020)

    Google Scholar 

  27. Gomez-Ojeda, R., Gonzalez-Jimenez, J.: Robust stereo visual odometry through a probabilistic combination of points and line segments. In: ICRA (2016)

    Google Scholar 

  28. Gomez-Ojeda, R., Moreno, F.A., Zuniga-Noël, D., Scaramuzza, D., Gonzalez-Jimenez, J.: Pl-slam: a stereo slam system through the combination of points and line segments. IEEE Trans. Rob. 35(3), 734–746 (2019)

    Article  Google Scholar 

  29. Hartley, R.I., Sturm, P.: Triangulation. Comput. Vis. Image Understand. (CVIU) 68(2), 146–157 (1997)

    Article  Google Scholar 

  30. He, X., et al.: Detector-free structure from motion. arXiv preprint arXiv:2306.15669 (2023)

  31. He, Y., Zhao, J., Guo, Y., He, W., Yuan, K.: Pl-vio: tightly-coupled monocular visual-inertial odometry using point and line features. Sensors 18(4), 1159 (2018)

    Article  Google Scholar 

  32. Hofer, M., Maurer, M., Bischof, H.: Line3d: efficient 3d scene abstraction for the built environment. In: German Conference on Pattern Recognition (2015)

    Google Scholar 

  33. Holynski, A., Geraghty, D., Frahm, J.M., Sweeney, C., Szeliski, R.: Reducing drift in structure from motion using extended features. In: International Conference on 3D Vision (3DV) (2020)

    Google Scholar 

  34. Huang, S., Qin, F., Xiong, P., Ding, N., He, Y., Liu, X.: TP-LSD: tri-points based line segment detector. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 770–785. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58583-9_46

    Chapter  Google Scholar 

  35. Jakob, W., Rhinelander, J., Moldovan, D.: pybind11 – seamless operability between c++11 and python. https://github.com/pybind/pybind11

  36. Jiang, N., Cui, Z., Tan, P.: A global linear method for camera pose registration. In: ICCV (2013)

    Google Scholar 

  37. Jin, Y., et al.: Image matching across wide baselines: from paper to practice. IJCV 129(2), 517–547 (2021)

    Article  Google Scholar 

  38. Kanatani, K.: For geometric inference from images, what kind of statistical model is necessary? Syst. Comput. Japan 35(6), 1–9 (2004)

    Article  Google Scholar 

  39. Kanazawa, Y., Kanatani, K.: Do we really have to consider covariance matrices for image feature points?. Electr. Commun. Japan (part III: Fundamental Electr. Sci.) 86(1), 1–10 (2003)

    Google Scholar 

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

    Google Scholar 

  41. Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. (ToG) 42(4), 1–14 (2023)

    Article  Google Scholar 

  42. Kuhn, A., Sormann, C., Rossi, M., Erdler, O., Fraundorfer, F.: Deepc-mvs: deep confidence prediction for multi-view stereo reconstruction. In: International Conference on 3D Vision (3DV) (2020)

    Google Scholar 

  43. Kukelova, Z., Bujnak, M., Pajdla, T.: Closed-form solutions to minimal absolute pose problems with known vertical direction. In: ACCV (2010)

    Google Scholar 

  44. Kushal, A., Agarwal, S.: Visibility based preconditioning for bundle adjustment. In: CVPR (2012)

    Google Scholar 

  45. Larsson, V.: PoseLib - Minimal Solvers for Camera Pose Estimation. https://github.com/vlarsson/PoseLib

  46. Lebeda, K., Matas, J., Chum, O.: Fixing the locally optimized ransac–full experimental evaluation. In: BMVC (2012)

    Google Scholar 

  47. Li, H., Zhao, J., Bazin, J.C., Chen, W., Liu, Z., Liu, Y.H.: Quasi-globally optimal and efficient vanishing point estimation in manhattan world. In: ICCV (2019)

    Google Scholar 

  48. Li, Z., et al.: Neuralangelo: high-fidelity neural surface reconstruction. In: CVPR (2023)

    Google Scholar 

  49. Lim, H., Jeon, J., Myung, H.: Uv-slam: unconstrained line-based slam using vanishing points for structural mapping. IEEE Robot. Autom. Lett. (RA-L) 7(2), 1518–1525 (2022)

    Google Scholar 

  50. Lim, H., Kim, Y., Jung, K., Hu, S., Myung, H.: Avoiding degeneracy for monocular visual slam with point and line features. In: ICRA (2021)

    Google Scholar 

  51. Lindenberger, P., Sarlin, P.E., Larsson, V., Pollefeys, M.: Pixel-perfect structure-from-motion with featuremetric refinement. In: ICCV (2021)

    Google Scholar 

  52. Liu, S., Yu, Y., Pautrat, R., Pollefeys, M., Larsson, V.: 3d line mapping revisited. In: CVPR (2023)

    Google Scholar 

  53. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  54. Lu, F., Hartley, R.: A fast optimal algorithm for l 2 triangulation. In: ACCV (2007)

    Google Scholar 

  55. Marzorati, D., Matteucci, M., Migliore, D., Sorrenti, D.G.: Integration of 3d lines and points in 6dof visual slam by uncertain projective geometry. In: EMCR (2007)

    Google Scholar 

  56. Mateus, A., Tahri, O., Aguiar, A.P., Lima, P.U., Miraldo, P.: On incremental structure from motion using lines. IEEE Trans. Rob. 38(1), 391–406 (2021)

    Article  Google Scholar 

  57. Meidow, J., Beder, C., Förstner, W.: Reasoning with uncertain points, straight lines, and straight line segments in 2d. ISPRS J. Photogramm. Remote. Sens. 64(2), 125–139 (2009)

    Article  Google Scholar 

  58. Micusik, B., Wildenauer, H.: Structure from motion with line segments under relaxed endpoint constraints. IJCV 124(1), 65–79 (2017)

    Article  MathSciNet  Google Scholar 

  59. 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 

  60. Moulon, P., Monasse, P., Perrot, R., Marlet, R.: OpenMVG: open multiple view geometry. In: International Workshop on Reproducible Research in Pattern Recognition (2016)

    Google Scholar 

  61. Muhle, D., Koestler, L., Jatavallabhula, K.M., Cremers, D.: Learning correspondence uncertainty via differentiable nonlinear least squares. In: CVPR (2023)

    Google Scholar 

  62. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  63. Nistér, D.: Preemptive ransac for live structure and motion estimation. Mach. Vis. Appl. 16(5), 321–329 (2005)

    Article  Google Scholar 

  64. Nurutdinova, I., Fitzgibbon, A.: Towards pointless structure from motion: 3d reconstruction and camera parameters from general 3d curves. In: ICCV (2015)

    Google Scholar 

  65. Pautrat, R., Lin, J.T., Larsson, V., Oswald, M.R., Pollefeys, M.: Sold2: self-supervised occlusion-aware line description and detection. In: CVPR (2021)

    Google Scholar 

  66. Pautrat, R., Liu, S., Hruby, P., Pollefeys, M., Barath, D.: Vanishing point estimation in uncalibrated images with prior gravity direction. In: ICCV (2023)

    Google Scholar 

  67. Pautrat, R., Suárez, I., Yu, Y., Pollefeys, M., Larsson, V.: Gluestick: robust image matching by sticking points and lines together. In: ICCV (2023)

    Google Scholar 

  68. Pautrat, R., Barath, D., Larsson, V., Oswald, M.R., Pollefeys, M.: Deeplsd: line segment detection and refinement with deep image gradients. In: CVPR (2023)

    Google Scholar 

  69. Persson, M., Nordberg, K.: Lambda twist: An accurate fast robust perspective three point (p3p) solver. In: ECCV (2018)

    Google Scholar 

  70. Poggi, M., Mattoccia, S.: Learning from scratch a confidence measure. In: BMVC (2016)

    Google Scholar 

  71. Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F.: Pl-slam: Real-time monocular visual slam with points and lines. In: ICRA (2017)

    Google Scholar 

  72. Qian, G., Chellappa, R.: Structure from motion using sequential monte carlo methods. IJCV 59, 5–31 (2004)

    Article  Google Scholar 

  73. Qian, Y., Elder, J.H.: A reliable online method for joint estimation of focal length and camera rotation. In: ECCV (2022)

    Google Scholar 

  74. Roberts, M., Ramapuram, J., Ranjan, A., Kumar, A., Bautista, M.A., Paczan, N., Webb, R., Susskind, J.M.: Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In: ICCV (2021)

    Google Scholar 

  75. Sarlin, P.E.: Visual localization made easy with hloc. https://github.com/cvg/Hierarchical-Localization/

  76. Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: Superglue: Learning feature matching with graph neural networks. In: CVPR (2020)

    Google Scholar 

  77. Sarlin, P.E., et al.: LaMAR: benchmarking Localization and Mapping for Augmented Reality. In: ECCV (2022)

    Google Scholar 

  78. Schindler, G., Krishnamurthy, P., Dellaert, F.: Line-based structure from motion for urban environments. In: International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT) (2006)

    Google Scholar 

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

    Google Scholar 

  80. Schöps, T., Engel, J., Cremers, D.: Semi-dense visual odometry for ar on a smartphone. In: International Symposium on Mixed and Augmented Reality (ISMAR) (2014)

    Google Scholar 

  81. Schops, T., et al.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: CVPR (2017)

    Google Scholar 

  82. Seki, A., Pollefeys, M.: Patch based confidence prediction for dense disparity map. In: BMVC (2016)

    Google Scholar 

  83. 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 

  84. Shu, F., Wang, J., Pagani, A., Stricker, D.: Structure plp-slam: efficient sparse mapping and localization using point, line and plane for monocular, rgb-d and stereo cameras. In: ICRA (2023)

    Google Scholar 

  85. Sinha, S.N., Steedly, D., Szeliski, R.: A multi-stage linear approach to structure from motion. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6554, pp. 267–281. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35740-4_21

    Chapter  Google Scholar 

  86. Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: ACM SIGGRAPH (2006)

    Google Scholar 

  87. Steedly, D., Essa, I.A., Dellaert, F.: Spectral partitioning for structure from motion. In: ICCV (2003)

    Google Scholar 

  88. Steele, R.M., Jaynes, C.: Feature uncertainty arising from covariant image noise. In: CVPR (2005)

    Google Scholar 

  89. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of rgb-d slam systems. In: IROS (2012)

    Google Scholar 

  90. Sweeney, C.: Theia multiview geometry library: Tutorial & reference. http://theia-sfm.org

  91. Sweeney, C., Sattler, T., Hollerer, T., Turk, M., Pollefeys, M.: Optimizing the viewing graph for structure-from-motion. In: ICCV (2015)

    Google Scholar 

  92. Tang, C., Tan, P.: Ba-net: dense bundle adjustment network. In: International Conference on Learning Representations (ICLR) (2019)

    Google Scholar 

  93. Taylor, C.J., Kriegman, D.J.: Structure and motion from line segments in multiple images. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 17(11), 1021–1032 (1995)

    Google Scholar 

  94. Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-linkage. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 537–547. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_41

    Chapter  Google Scholar 

  95. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment — a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) IWVA 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44480-7_21

    Chapter  Google Scholar 

  96. Von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: Lsd: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 32(4), 722–732 (2008)

    Google Scholar 

  97. Wang, J., Karaev, N., Rupprecht, C., Novotny, D.: Visual geometry grounded deep structure from motion. In: CVPR (2024)

    Google Scholar 

  98. Wang, J., Rupprecht, C., Novotny, D.: Posediffusion: solving pose estimation via diffusion-aided bundle adjustment. In: ICCV (2023)

    Google Scholar 

  99. Wang, S., Leroy, V., Cabon, Y., Chidlovskii, B., Revaud, J.: Dust3r: geometric 3d vision made easy. In: CVPR (2024)

    Google Scholar 

  100. Wei, X., Huang, J., Ma, X.: Real-time monocular visual slam by combining points and lines. In: IEEE International Conference on Multimedia and Expo (ICME) (2019)

    Google Scholar 

  101. Wilson, K., Snavely, N.: Robust global translations with 1DSfM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 61–75. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_5

    Chapter  Google Scholar 

  102. Wu, C.: Visualsfm: A visual structure from motion system (2011). http://www.cs.washington.edu/homes/ccwu/vsfm

  103. Wu, C.: Towards linear-time incremental structure from motion. In: International Conference on 3D Vision (3DV) (2013)

    Google Scholar 

  104. Xiao, Y., Xue, N., Wu, T., Xia, G.S.: Level-s2fm: structure from motion on neural level set of implicit surfaces. In: CVPR (2023)

    Google Scholar 

  105. Xue, N., et al.: Holistically-attracted wireframe parsing. In: CVPR (2020)

    Google Scholar 

  106. Yan, J., Zheng, Y., Yang, J., Mihaylova, L., Yuan, W., Gu, F.: Plpf-vslam: an indoor visual slam with adaptive fusion of point-line-plane features. J. Field Robot. (2023)

    Google Scholar 

  107. Yu, Z., Peng, S., Niemeyer, M., Sattler, T., Geiger, A.: Monosdf: exploring monocular geometric cues for neural implicit surface reconstruction. In: NeurIPS (2022)

    Google Scholar 

  108. Zeisl, B., Georgel, P.F., Schweiger, F., Steinbach, E.G., Navab, N., Munich, G.: Estimation of location uncertainty for scale invariant features points. In: BMVC (2009)

    Google Scholar 

  109. Zhang, H., Grießbach, D., Wohlfeil, J., Börner, A.: Uncertainty model for template feature matching. In: Paul, M., Hitoshi, C., Huang, Q. (eds.) PSIVT 2017. LNCS, vol. 10749, pp. 406–420. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75786-5_33

    Chapter  Google Scholar 

  110. Zhang, J.Y., Lin, A., Kumar, M., Yang, T.H., Ramanan, D., Tulsiani, S.: Cameras as rays: pose estimation via ray diffusion. In: International Conference on Learning Representations (ICLR) (2024)

    Google Scholar 

  111. Zhang, L., Lu, H., Hu, X., Koch, R.: Vanishing point estimation and line classification in a manhattan world with a unifying camera model. IJCV 117 (2015)

    Google Scholar 

  112. Zhao, W., Liu, S., Wei, Y., Guo, H., Liu, Y.J.: A confidence-based iterative solver of depths and surface normals for deep multi-view stereo. In: ICCV (2021)

    Google Scholar 

  113. Zhou, L., Ye, J., Kaess, M.: A stable algebraic camera pose estimation for minimal configurations of 2d/3d point and line correspondences. In: ACCV (2018)

    Google Scholar 

  114. Zuo, X., Xie, X., Liu, Y., Huang, G.: Robust visual slam with point and line features. In: IROS (2017)

    Google Scholar 

Download references

Acknowledgements

This work has been supported by Innosuisse funding (Grant No. 100.567 IP-ICT). V. Larsson was supported by ELLIIT and the Swedish Research Council (Grant No. 2023–05424).

Author information

Authors and Affiliations

  1. ETH Zurich, Zurich, Switzerland

    Shaohui Liu, Yidan Gao, Tianyi Zhang & Marc Pollefeys

  2. Microsoft Mixed Reality and AI Lab Zurich, Zurich, Switzerland

    Rémi Pautrat, Johannes L. Schönberger & Marc Pollefeys

  3. Lund University, Lund, Sweden

    Viktor Larsson

Authors
  1. Shaohui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yidan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tianyi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rémi Pautrat
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johannes L. Schönberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Viktor Larsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marc Pollefeys
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shaohui Liu .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 5100 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, S. et al. (2025). Robust Incremental Structure-from-Motion with Hybrid Features. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15094. Springer, Cham. https://doi.org/10.1007/978-3-031-72764-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72764-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72763-4

  • Online ISBN: 978-3-031-72764-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics