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LCLD: A lightweight vanishing point detector with contrast-learning-based intermediate supervision module

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Abstract

Vanishing point detection is crucial in 3D vision, enabling the extraction of 3D information from 2D images. However, many vanishing point detectors involve a trade-off between model complexity and detection accuracy. To address this problem, we propose a lightweight vanishing point detector with intermediate supervision and a classifier for channel aggregation (CIAP). The proposed approach has the following novelties. Firstly, the intermediate supervision module leverages contrast learning, which learns by bringing similar samples closer and pushing dissimilar ones apart, with an extremely positive and negative sample selection strategy. Secondly, the fully connected layers are replaced with purely convolutional layers that aggregate multi-channel information, reducing the model parameters from \(\varvec{22M}\) to \(\varvec{4.6M}\) without compromising accuracy. Extensive validation on synthetic and real-world datasets shows the strong performance of our approach, with a \(\varvec{5.4\%}\) improvement in angle accuracy \(\varvec{0.2^{\circ }}\) over the state-of-the-art method VaPiD on the synthetic dataset. The reduced parameter count supports energy-efficient systems, contributing to the development of sustainable and scalable 3D vision solutions.

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References

  1. Davison AJ, Reid ID, Molton ND, Stasse O (2007) Monoslam: Real-time single camera slam. IEEE Trans Pattern Anal Mach Intell 29(6):1052–1067

    Article  Google Scholar 

  2. Lee S, Kim J, Shin Yoon J, Shin S, Bailo O, Kim N, Lee T-H, Seok Hong H, Han S-H, So Kweon I (2017) Vpgnet: Vanishing point guided network for lane and road marking detection and recognition. In: Proceedings of the IEEE international conference on computer vision, pp 1947–1955

  3. Guillou E, Meneveaux D, Maisel E, Bouatouch K (2000) Using vanishing points for camera calibration and coarse 3d reconstruction from a single image. Visual Comput 16(7):396–410

    Article  MATH  Google Scholar 

  4. Cipolla R, Drummond T, Robertson DP (1999) Camera calibration from vanishing points in image of architectural scenes. BMVC, Citeseer 99:382–391

    MATH  Google Scholar 

  5. Grammatikopoulos L, Karras G, Petsa E (2007) An automatic approach for camera calibration from vanishing points. ISPRS J Photogrammetry Remote Sens 62(1):64–76

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  7. Zhou Z, Farhat F, Wang JZ (2017) Detecting dominant vanishing points in natural scenes with application to composition-sensitive image retrieval. IEEE Trans Multimed 19(12):2651–2665

    Article  MATH  Google Scholar 

  8. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  MATH  Google Scholar 

  9. Denis P, Elder JH, Estrada FJ (2008) Efficient edge-based methods for estimating manhattan frames in urban imagery. In: European conference on computer vision, Springer, pp 197–210

  10. Hough PV (1962) Method and means for recognizing complex patterns. Google Patents. US Patent 3,069,654

  11. Zhai M, Workman S, Jacobs N (2016) Detecting vanishing points using global image context in a non-manhattan world. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5657–5665

  12. Chang C-K, Zhao J, Itti L (2018) Deepvp: Deep learning for vanishing point detection on 1 million street view images. In: 2018 IEEE International conference on robotics and automation (ICRA), IEEE, pp 4496–4503

  13. Liu Y-B, Zeng M, Meng Q-H (2020) D-vpnet: A network for real-time dominant vanishing point detection in natural scenes. Neurocomputing 417:432–440

    Article  MATH  Google Scholar 

  14. Lin Y, Wiersma R, Pintea SL, Hildebrandt K, Eisemann E, Gemert JC (2022) Deep vanishing point detection: Geometric priors make dataset variations vanish. arXiv:2203.08586

  15. Zhou Y, Qi H, Huang J, Ma Y (2019) Neurvps: Neural vanishing point scanning via conic convolution. Adv Neural Inf Process Syst 32

  16. Liu S, Zhou Y, Zhao Y (2021) Vapid: A rapid vanishing point detector via learned optimizers. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 12859–12868

  17. Wu Z, Xiong Y, Yu SX, Lin D (2018) Unsupervised feature learning via non-parametric instance discrimination. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3733–3742

  18. Tian Y, Krishnan D, Isola P (2020) Contrastive multiview coding. In: European conference on computer vision, Springer, pp 776–794

  19. He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 9729–9738

  20. Grill J-B, Strub F, Altché F, Tallec C, Richemond P, Buchatskaya E, Doersch C, Avila Pires B, Guo Z, Gheshlaghi Azar M et al (2020) Bootstrap your own latent-a new approach to self-supervised learning. Adv Neural Inf Process Syst 33:21271–21284

    Google Scholar 

  21. Choi H-S, An K, Kang M (2019) Regression with residual neural network for vanishing point detection. Image Vision Comput 91:103797

    Article  MATH  Google Scholar 

  22. Hough PV, Powell BW (1960) A method for faster analysis of bubble chamber photographs. Il Nuovo Cimento (1955-1965) 18(6):1184–1191

  23. Lu X, Yaoy J, Li H, Liu Y, Zhang X (2017) 2-line exhaustive searching for real-time vanishing point estimation in manhattan world. In: 2017 IEEE Winter conference on applications of computer vision (WACV), IEEE, pp 345–353

  24. Schindler G, Dellaert F (2004) Atlanta world: An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004., IEEE, vol. 1

  25. Lezama J, Gioi R, Randall G, Morel J-M (2014) Finding vanishing points via point alignments in image primal and dual domains. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 509–515

  26. Xu Y, Oh S, Hoogs A (2013) A minimum error vanishing point detection approach for uncalibrated monocular images of man-made environments. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1376–1383

  27. Tuytelaars T, Van Gool L, Proesmans M, Moons T (1998) The cascaded hough transform as an aid in aerial image interpretation. In: Sixth international conference on computer vision (IEEE Cat. No. 98CH36271), IEEE, pp 67–72

  28. Tardif J-P (2009) Non-iterative approach for fast and accurate vanishing point detection. In: 2009 IEEE 12th International conference on computer vision, IEEE, pp 1250–1257

  29. Wu J, Zhang L, Liu Y, Chen K (2021) Real-time vanishing point detector integrating under-parameterized ransac and hough transform. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3732–3741

  30. Shuai Y, Tiantian Y, Guodong Y, Zize L (2017) Regression convolutional network for vanishing point detection. In: 2017 32nd Youth academic annual conference of chinese association of automation (YAC), IEEE, pp 634–638

  31. Zhou Y, Qi H, Zhai Y, Sun Q, Chen Z, Wei L-Y, Ma Y (2019) Learning to reconstruct 3d manhattan wireframes from a single image. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 7698–7707

  32. Tong X, Ying X, Shi Y, Wang R, Yang J (2022) Transformer based line segment classifier with image context for real-time vanishing point detection in manhattan world. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6093–6102

  33. Oord Avd, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding. arXiv:1807.03748

  34. Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, PMLR, pp 1597–1607

  35. Chuang C-Y, Robinson J, Lin Y-C, Torralba A, Jegelka S (2020) Debiased contrastive learning. Adv Neural Inf Process Syst 33:8765–8775

    MATH  Google Scholar 

  36. Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inf Process Syst 33:18661–18673

  37. Xia S-Y, Lv J, Xu N, Geng X (2022) Ambiguity-induced contrastive learning for instance-dependent partial label learning

  38. Barnard ST (1983) Interpreting perspective images. Artif Intell 21(4):435–462

    Article  MATH  Google Scholar 

  39. Shah A, Sra S, Chellappa R, Cherian A (2021) Max-margin contrastive learning. arXiv:2112.11450

  40. Feng C, Deng F, Kamat VR (2010) Semi-automatic 3d reconstruction of piecewise planar building models from single image. CONVR (Sendai:) 2(5):6

  41. Li H, Zhao J, Bazin J-C, Chen W, Liu Z, Liu Y-H (2019) Quasi-globally optimal and efficient vanishing point estimation in manhattan world. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 1646–1654

  42. Kluger F, Brachmann E, Ackermann H, Rother C, Yang MY, Rosenhahn B (2020) Consac: Robust multi-model fitting by conditional sample consensus. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4634–4643

  43. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980

  44. González Á (2010) Measurement of areas on a sphere using fibonacci and latitude-longitude lattices. Math Geosci 42(1):49–64

    Article  MathSciNet  MATH  Google Scholar 

  45. Simon G, Fond A, Berger M-O (2018) A-contrario horizon-first vanishing point detection using second-order grouping laws. In: Proceedings of the European conference on computer vision (ECCV), pp 318–333

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Acknowledgements

This work is supported by the Guangdong Basic and Applied Basic Research Foundation(No. 2023A1515140132).

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Correspondence to Lianping Yang.

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Yang, L., Huang, W., Zhao, X. et al. LCLD: A lightweight vanishing point detector with contrast-learning-based intermediate supervision module. Appl Intell 55, 79 (2025). https://doi.org/10.1007/s10489-024-05949-2

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