Abstract
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently risen to prominence for their exceptional ability to reconstruct scenes but are hampered by slow inference speed, prolonged training, and inconsistent depth estimation. Some previous work utilizes ground truth depth for optimization but it is hard to acquire in the surgical domain. To overcome these obstacles, we present Endo-4DGS, a real-time endoscopic dynamic reconstruction approach that utilizes 3D Gaussian Splatting (GS) for 3D representation. Specifically, we propose lightweight MLPs to capture temporal dynamics with Gaussian deformation fields. To obtain a satisfactory Gaussian Initialization, we exploit a powerful depth estimation foundation model, Depth-Anything, to generate pseudo-depth maps as a geometry prior. We additionally propose confidence-guided learning to tackle the ill-pose problems in monocular depth estimation and enhance the depth-guided reconstruction with surface normal constraints and depth regularization. Our approach has been validated on two surgical datasets, where it can effectively render in real-time, compute efficiently, and reconstruct with remarkable accuracy. Our code is available at https://github.com/lastbasket/Endo-4DGS.
Y. Huang, B. Cui and L. Bai—Co-first authors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bae, G., Budvytis, I., Yeung, C.K., Cipolla, R.: Deep multi-view stereo for dense 3d reconstruction from monocular endoscopic video. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 774–783. Springer (2020)
Barbed, O.L., Montiel, J.M., Fua, P., Murillo, A.C.: Tracking adaptation to improve superpoint for 3d reconstruction in endoscopy. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 583–593. Springer (2023)
Cao, A., Johnson, J.: Hexplane: A fast representation for dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 130–141 (2023)
Chen, G., Wang, W.: A survey on 3d gaussian splatting. arXiv preprint arXiv:2401.03890 (2024)
Cheng, K., Long, X., Yang, K., Yao, Y., Yin, W., Ma, Y., Wang, W., Chen, X.: Gaussianpro: 3d gaussian splatting with progressive propagation. arXiv preprint arXiv: (2024)
Chung, J., Oh, J., Lee, K.M.: Depth-regularized optimization for 3d gaussian splatting in few-shot images. arXiv preprint arXiv:2311.13398 (2023)
Cui, B., Islam, M., Bai, L., Ren, H.: Surgical-dino: Adapter learning of foundation model for depth estimation in endoscopic surgery. arXiv preprint arXiv:2401.06013 (2024)
Fang, J., Yi, T., Wang, X., Xie, L., Zhang, X., Liu, W., Nießner, M., Tian, Q.: Fast dynamic radiance fields with time-aware neural voxels. In: SIGGRAPH Asia 2022 Conference Papers. pp. 1–9 (2022)
Gao, H., Yang, X., Xiao, X., Zhu, X., Zhang, T., Hou, C., Liu, H., Meng, M.Q.H., Sun, L., Zuo, X., et al.: Transendoscopic flexible parallel continuum robotic mechanism for bimanual endoscopic submucosal dissection. The International Journal of Robotics Research p. 02783649231209338 (2023)
Hayoz, M., Hahne, C., Gallardo, M., Candinas, D., Kurmann, T., Allan, M., Sznitman, R.: Learning how to robustly estimate camera pose in endoscopic videos. International Journal of Computer Assisted Radiology and Surgery pp. 1185—1192 (2023)
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42(4) (2023)
Liu, Y., Li, C., Yang, C., Yuan, Y.: Endogaussian: Gaussian splatting for deformable surgical scene reconstruction. arXiv preprint arXiv:2401.12561 (2024)
Long, Y., Li, Z., Yee, C.H., Ng, C.F., Taylor, R.H., Unberath, M., Dou, Q.: E-dssr: efficient dynamic surgical scene reconstruction with transformer-based stereoscopic depth perception. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part IV 24. pp. 415–425. Springer (2021)
Mahmoud, N., Cirauqui, I., Hostettler, A., Doignon, C., Soler, L., Marescaux, J., Montiel, J.M.M.: Orbslam-based endoscope tracking and 3d reconstruction. In: Computer-Assisted and Robotic Endoscopy: Third International Workshop, CARE 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 3. pp. 72–83. Springer (2017)
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM 65(1), 99–106 (2021)
Ozyoruk, K.B., Gokceler, G.I., Bobrow, T.L., Coskun, G., Incetan, K., Almalioglu, Y., Mahmood, F., Curto, E., Perdigoto, L., Oliveira, M., et al.: Endoslam dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Medical image analysis 71, 102058 (2021)
Psychogyios, D., Colleoni, E., Van Amsterdam, B., Li, C.Y., Huang, S.Y., Li, Y., Jia, F., Zou, B., Wang, G., Liu, Y., et al.: Sar-rarp50: Segmentation of surgical instrumentation and action recognition on robot-assisted radical prostatectomy challenge. arXiv preprint arXiv:2401.00496 (2023)
Shao, S., Pei, Z., Chen, W., Zhu, W., Wu, X., Sun, D., Zhang, B.: Self-supervised monocular depth and ego-motion estimation in endoscopy: Appearance flow to the rescue. Medical image analysis 77, 102338 (2022)
Stucker, C., Schindler, K.: Resdepth: Learned residual stereo reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 184–185 (2020)
Wang, F., Chen, Z., Wang, G., Song, Y., Liu, H.: Masked space-time hash encoding for efficient dynamic scene reconstruction. Advances in Neural Information Processing Systems 36 (2024)
Wang, G., Chen, Z., Loy, C.C., Liu, Z.: Sparsenerf: Distilling depth ranking for few-shot novel view synthesis. IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
Wang, Y., Long, Y., Fan, S.H., Dou, Q.: Neural rendering for stereo 3d reconstruction of deformable tissues in robotic surgery. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 431–441. Springer (2022)
Wu, G., Yi, T., Fang, J., Xie, L., Zhang, X., Wei, W., Liu, W., Tian, Q., Xinggang, W.: 4d gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528 (2023)
Yang, C., Wang, K., Wang, Y., Yang, X., Shen, W.: Neural lerplane representations for fast 4d reconstruction of deformable tissues. arXiv preprint arXiv:2305.19906 (2023)
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Unleashing the power of large-scale unlabeled data. arXiv:2401.10891 (2024)
Yifan, W., Serena, F., Wu, S., Öztireli, C., Sorkine-Hornung, O.: Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG) 38(6), 1–14 (2019)
Zha, R., Cheng, X., Li, H., Harandi, M., Ge, Z.: Endosurf: Neural surface reconstruction of deformable tissues with stereo endoscope videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 13–23. Springer (2023)
Zhu, L., Wang, Z., Jin, Z., Lin, G., Yu, L.: Deformable endoscopic tissues reconstruction with gaussian splatting. arXiv preprint arXiv:2401.11535 (2024)
Zia, A., Bhattacharyya, K., Liu, X., Berniker, M., Wang, Z., Nespolo, R., Kondo, S., Kasai, S., Hirasawa, K., Liu, B., et al.: Surgical tool classification and localization: results and methods from the miccai 2022 surgtoolloc challenge. arXiv preprint arXiv:2305.07152 (2023)
Acknowledgements
This work was supported by Hong Kong RGC CRF C4026-21G, RIF R4020-22, GRF 14211420, 14216020 & 14203323); Shenzhen-Hong Kong-Macau Technology Research Programme (Type C) STIC Grant SGDX20210823103535014 (202108233000303).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, Y. et al. (2024). Endo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15006. Springer, Cham. https://doi.org/10.1007/978-3-031-72089-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-72089-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-72088-8
Online ISBN: 978-3-031-72089-5
eBook Packages: Computer ScienceComputer Science (R0)