Movatterモバイル変換


[0]ホーム

URL:


Skip to main content

Advertisement

Springer Nature Link
Log in

Depth Optimization for Accurate 3D Reconstruction from Light Field Images

  • Conference paper
  • First Online:

Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14426))

Abstract

Because the light field camera can capture both the position and direction of light simultaneously, it enables us to estimate the depth map from a single light field image and subsequently obtain the 3D point cloud structure. However, the reconstruction results based on light field depth estimation often contain holes and noisy points, which hampers the clarity of the reconstructed 3D object structure. In this paper, we propose a depth optimization algorithm to achieve a more accurate depth map. We introduce a depth confidence metric based on the photo consistency of the refocused angular sampling image. By utilizing this confidence metric, we detect the outlier points in the depth map and generate an outlier mask map. Finally, we optimize the depth map using the proposed energy function. Experimental results demonstrate the superiority of our method compared to other algorithms, particularly in addressing issues related to holes, boundaries, and noise.

This work is supported by National Key Research and Development Project Grant, Grant/Award Number: 2018AAA0100802.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 9151
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 11439
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Farhood, H., Perry, S., Cheng, E., Kim, J.: Enhanced 3d point cloud from a light field image. Remote Sens.12(7), 1125 (2020)

    Article  Google Scholar 

  2. Galea, C., Guillemot, C.: Denoising of 3d point clouds constructed from light fields. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1882–1886. IEEE (2019)

    Google Scholar 

  3. Han, D., Jiao, Z., Zhou, L., Ding, C., Wu, Y.: Geometric constraints based 3d reconstruction method of tomographic sar for buildings. Sci. China Inf. Sci.66(1), 1–13 (2023)

    Article MathSciNet  Google Scholar 

  4. Han, K., Xiang, W., Wang, E., Huang, T.: A novel occlusion-aware vote cost for light field depth estimation. IEEE Trans. Pattern Anal. Mach. Intell., 1–1 (2021)

    Google Scholar 

  5. Honauer, K., Johannsen, O., Kondermann, D., Goldluecke, B.: A dataset and evaluation methodology for depth estimation on 4d light fields. In: Asian Conference on Computer Vision (2016)

    Google Scholar 

  6. Hua, S., Liu, Q., Yin, G., Guan, X., Jiang, N., Zhang, Y.: Research on 3d medical image surface reconstruction based on data mining and machine learning. Int. J. Intell. Syst.37(8), 4654–4669 (2022)

    Article  Google Scholar 

  7. Kim, C., Zimmer, H., Pritch, Y., Sorkine-Hornung, A., Gross, M.: Scene reconstruction from high spatio-angular resolution light fields. ACM Trans. Graph.32(4), 1 (2013)

    Google Scholar 

  8. Peng, J., Xiong, Z., Zhang, Y., Liu, D., Wu, F.: Lf-fusion: dense and accurate 3d reconstruction from light field images. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1–4. IEEE (2017)

    Google Scholar 

  9. Perra, C., Murgia, F., Giusto, D.: An analysis of 3d point cloud reconstruction from light field images. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1–6. IEEE (2016)

    Google Scholar 

  10. Raj, A.S., Lowney, M., Shah, R., Wetzstein, G.: Stanford light field archives (2016).http://lightfields.stanford.edu/

  11. Ren, N., Levoy, M., Bredif, M., Duval, G., Hanrahan, P.: Light field photography with a hand-held plenoptic camera. Stanford University Cstr (2005)

    Google Scholar 

  12. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: International Conference on Computer Vision (2002)

    Google Scholar 

  13. Tsai, Y.J., Liu, Y.L., Ouhyoung, M., Chuang, Y.Y.: Attention-based view selection networks for light-field disparity estimation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12095–12103 (2020)

    Google Scholar 

  14. Wang, T.C., Efros, A.A., Ramamoorthi, R.: Depth estimation with occlusion modeling using light-field cameras. IEEE Trans. Pattern Anal. Mach. Intell.38(11), 2170–2181 (2016)

    Article  Google Scholar 

  15. Wang, Y., Wang, L., Liang, Z., Yang, J., An, W., Guo, Y.: Occlusion-aware cost constructor for light field depth estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19809–19818 (June 2022)

    Google Scholar 

  16. Wanner, S., Meister, S., Goldluecke, B.: Datasets and benchmarks for densely sampled 4d light fields. In: Vision, Modeling and Visualization, pp. 225–226 (2013)

    Google Scholar 

  17. Zhang, L., Liu, L., Chai, B., Xu, M., Song, Y.: Multi-resolution 3d reconstruction of cultural landscape heritage based on cloud computing and hd image data. J. Intell. Fuzzy Syst.39(4), 5097–5107 (2020)

    Article  Google Scholar 

  18. Zhang, S., Sheng, H., Li, C., Zhang, J., Xiong, Z.: Robust depth estimation for light field via spinning parallelogram operator. Comput. Vis. Image Underst.145, 148–159 (2016)

    Article  Google Scholar 

  19. Zhao, H., Liu, Y., Wei, L., Wang, Y.: Superpixel-based optimization for point cloud reconstruction from light field. In: 2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–6. IEEE (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China

    Xuechun Wang, Wentao Chao & Fuqing Duan

Authors
  1. Xuechun Wang

    You can also search for this author inPubMed Google Scholar

  2. Wentao Chao

    You can also search for this author inPubMed Google Scholar

  3. Fuqing Duan

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toFuqing Duan.

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

Rights and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, X., Chao, W., Duan, F. (2024). Depth Optimization for Accurate 3D Reconstruction from Light Field Images. In: Liu, Q.,et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14426. Springer, Singapore. https://doi.org/10.1007/978-981-99-8432-9_7

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /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
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 9151
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 11439
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only


[8]ページ先頭

©2009-2025 Movatter.jp