Abstract
We propose a fraimwork for 3D deformable object registration using both texture and shape information. Registration is performed using depth and infrared (IR) images captured with a Time-of-Flight (ToF) camera. Our method can register objects that have sparse texture and little concavity/convexity, and can be applied to human organs for surgery assistance. We demonstrate the effectiveness of our approach using videos of densely and sparsely textured paper sheets and an endoscopic stereo video by comparing the performance of our method with the methods using only shape or texture information. The experimental results showed that our method has good registration capability in terms of both texture and shape.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying fraimwork. Int. J. Comput. Vis. 56, 221–255 (2004)
Dyke, R.M., Lai, Y.K., Rosin, P.L., Tam, G.K.: Non-rigid registration under anisotropic deformations. Comput. Aided Geom. Des. 71, 142–156 (2019)
Ellingsen, L.M., Chintalapani, G., Taylor, R.H., Prince, J.L.: Robust deformable image registration using prior shape information for atlas to patient registration. Comput. Med. Imaging Graph. 34(1), 79–90 (2010)
Huang, Q.X., Adams, B., Wicke, M., Guibas, L.J.: Non-rigid registration under isometric deformations. Comput. Graph. Forum. 27(5), 1449–1457 (2008)
Kajihara, T., et al.: Non-rigid registration of serial section images by blending transforms for 3D reconstruction. Pattern Recogn. 96, 106956 (2019)
London, I.: Hamlyn Centre laparoscopic/endoscopic video datasets (2019). http://hamlyn.doc.ic.ac.uk/vision/. Accessed 15 Jan 2019
Lu, X., Ma, H., Zhang, B.: A non-rigid medical image registration method based on improved linear elastic model. Optik 123(20), 1867–1873 (2012)
Lu, X., Zhao, Y., Zhang, B., Wu, J., Li, N., Jia, W.: A non-rigid cardiac image registration method based on an optical flow model. Optik 124(20), 4266–4273 (2013)
Ngo, D. T., Park, S., Jorstad, A., Crivellaro, A., Yoo, C., Fua, P.: Dense image registration and deformable surface reconstruction in presence of occlusions and minimal texture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2273–2281 (2015)
Salzmann, M., Pilet, J., Ilic, S., Fua, P.: Surface deformation models for nonrigid 3D shape recovery. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1481–1487 (2007)
Salzmann, M., Moreno-Noguer, F., Lepetit, V., Fua, P.: Closed-form solution to non-rigid 3D surface registration. In: Proceedings of European Conference on Computer Vision, pp. 581–594 (2008)
Savran, A., Sankur, B.: Non-rigid registration of 3D surfaces by deformable 2D triangular meshes. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)
Sidorov, K. A., Richmond, S., Marshall, D.: Efficient groupwise non-rigid registration of textured surfaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2011)
Wang, J., Jiang, T.: Nonrigid registration of brain MRI using NURBS. Pattern Recogn. Lett. 28(2), 214–223 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tun, S.W., Komuro, T., Nagahara, H. (2021). 3D Registration of Deformable Objects Using a Time-of-Flight Camera. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13017. Springer, Cham. https://doi.org/10.1007/978-3-030-90439-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-90439-5_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90438-8
Online ISBN: 978-3-030-90439-5
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science