AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (19.6 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

FusionMLS: Highly dynamic 3D reconstruction with consumer-grade RGB-D cameras

Department of Information and Computer Science,Keio University, Yokohama, Japan.
Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
LIGM, UMR 8049, Université Paris-Est Marne-la-Vallée, Champs-sur-Marne, France.
Japanese-French Laboratory for Informatics, CNRS, UMI 3527, Tokyo, Japan.
Show Author Information

Abstract

Multi-view dynamic three-dimensional reconstruction has typically required the use of custom shutter-synchronized camera rigs in order to capture scenes containing rapid movements or complex topology changes. In this paper, we demonstrate that multiple unsynchronized low-cost RGB-D cameras can be used for the same purpose. To alleviate issues caused by unsynchronized shutters, we propose a novel depth frame interpolation technique that allows synchronized data capture from highly dynamic 3D scenes. To manage the resulting huge number of input depth images, we also introduce an efficient moving least squares-based volumetric reconstruction method that generates triangle meshes of the scene. Our approach does not store the reconstruction volume in memory, making it memory-efficient and scalable to large scenes. Our implementation is completely GPU based and works in real time. The results shown herein, obtained with real data, demonstrate the effectiveness of our proposed method and its advantages compared to state-of-the-art approaches.

Electronic Supplementary Material

Video
CVM_2018_4_287-303_ESM.mp4

References

[1]
J. De Reu,; G. Plets,; G. Verhoeven,; P. D. Smedt,; M. Bats,; B. Cherretté,; W. D. Maeyer,; J. Deconynck,; D. Herremans,; P. Laloo,; M. V. Meirvenne,; W. D. Clercq, Towards a three-dimensional cost-effective registration of the archaeological heritage. Journal of Archaeological Science Vol. 40, No. 2, 1108-1121, 2013.
[2]
Y. Rong,; Y. Zheng,; T. Shao,; Y. Yang,; K. Zhou, An interactive approach for functional prototype recovery from a single RGBD image. Computational Visual Media Vol. 2, No. 1, 87-96, 2016.
[3]
K. Chen,; Y.-K. Lai,; S.-M. Hu, 3D indoor scene modeling from RGB-D data: A survey. Computational Visual Media Vol. 1, No. 4, 267-278, 2015.
[4]
S. Orts-Escolano,; C. Rhemann,; S. Fanello,; W. Chang,; A. Kowdle,; Y. Degtyarev,; D. Kim,; P. L. Davidson,; S. Khamis,; M. Dou,; V. Tankovich,; C. Loop,; Q. Cai,; P. A. Chou,; S. Mennicken,; J. Valentin,; V. Pradeep,; S. Wang,; S. B. Kang,; P. Kohli,; Y. Lutchyn,; C. Keskin,; S. Izadi, Holoportation: Virtual 3D teleportation in real-time. In: Proceedings of the 29th Annual Symposium on User Interface Software and Technology, 741-754, 2016.
[5]
M. Zollhöfer,; M. Nießner,; S. Izadi,; C. Rehmann,; C. Zach,; M. Fisher,; C. Wu,; A. Fitzgibbon,; C. Loop,; C. Theobalt,; M. Stamminger, Real-time non-rigid reconstruction using an RGB-D camera. ACM Transactions on Graphics Vol. 33, No. 4, Article No. 156, 2014.
[6]
R. A. Newcombe,; D. Fox,; S. M. Seitz, DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 343-352, 2015.
[7]
M. Innmann,; M. Zollhöfer,; M. Nießner,; C. Theobalt,; M. Stamminger, VolumeDeform: Real-time volumetric non-rigid reconstruction. In: Computer Vision-ECCV 2016. Lecture Notes in Computer Science, Vol. 9912. B. Leibe,; J. Matas,; N. Sebe,; M. Welling, Eds. Springer Cham, 362-379, 2016.
[8]
T. Yu,; K. Guo,; F. Xu,; Y. Dong,; Z. Su,; J. Zhao,; J. Li,; Q. Dai,; Y. Liu, BodyFusion: Real-time capture of human motion and surface geometry using a single depth camera. In: Proceedings of the IEEE International Conference on Computer Vision, 910-919, 2017.
[9]
A. Collet,; M. Chuang,; P. Sweeney,; D. Gillett,; D. Evseev,; D. Calabrese,; H. Hoppe,; A. Kirk,; S. Sullivan, High-quality streamable free-viewpoint video. ACM Transactions on Graphics Vol. 34, No. 4, Article No. 69, 2015.
[10]
M. Dou,; S. Khamis,; Y. Degtyarev,; P. Davidson,; S. R. Fanello,; A. Kowdle,; S. O. Escolano,; C. Rhemann,; D. Kim,; J. Taylor,; P. Kohli,; V. Tankovich,; S. Izadi, Fusion4D: Real-time performance capture of challenging scenes. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 114, 2016.
[11]
M. Dou,; P. Davidson,; S. R. Fanello,; S. Khamis,; A. Kowdle,; C. Rhemann,; V. Tankovich,; S. Izadi, Motion2fusion: Real-time volumetric performance capture. ACM Transactions on Graphics Vol. 36, No. 6, Article No. 246, 2017.
[12]
M. Nießner,; M. Zollhöfer,; S. Izadi,; M. Stamminger, Real-time 3D reconstruction at scale using voxel hashing. ACM Transactions on Graphics Vol. 32, No. 6, Article No. 169, 2013.
[13]
M. Berger,; A. Tagliasacchi,; L. M. Seversky,; P. Alliez,; G. Guennebaud,; J. A. Levine,; A. Sharf,; C. T. Silva, A survey of surface reconstruction from point clouds. Computer Graphics Forum Vol. 36, No. 1, 301-329, 2017.
[14]
Z. Li,; Y. Ji,; W. Yang,; J. Ye,; J. Yu, Robust 3D human motion reconstruction via dynamic template construction. In: Proceedings of the International Conference on 3D Vision, 496-505, 2017.
[15]
K. Wang,; G. Zhang,; S. Xia, Templateless non-rigid reconstruction and motion tracking with a single RGBD camera. IEEE Transactions on Image Processing Vol. 26, No. 12, 5966-5979, 2017.
[16]
B. Curless,; M. Levoy, A volumetric method for building complex models from range images. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, 303-312, 1996.
[17]
K. Guo,; F. Xu,; T. Yu,; X. Liu,; Q. Dai,; Y. Liu, Real-time geometry, albedo, and motion reconstruction using a single RGB-D camera. ACM Transactions on Graphics Vol. 36, No. 3, Article No. 32, 2017.
[18]
H. Zhang,; F. Xu, MixedFusion: Real-time reconstruction of an indoor scene with dynamic objects. IEEE Transactions on Visualization and Computer Graphics , 2018.
[19]
M. Kazhdan,; H. Hoppe, Screened Poisson surface reconstruction. ACM Transactions on Graphics Vol. 32, No. 3, Article No. 29, 2013.
[20]
R. Wang,; L. Wei,; E. Vouga,; Q. Huang,; D. Ceylan,; G. Medioni,; H. Li, Capturing dynamic textured surfaces of moving targets. In: Computer Vision-ECCV 2016. B. Leibe,; J. Matas,; N. Sebe,; M. Welling, Eds. Springer Cham, 271-288, 2016.
[21]
D. S. Alexiadis,; N. Zioulis,; D. Zarpalas,; P. Daras Fast deformable model-based human performance capture and FVV using consumer-grade RGB-D sensors. Pattern Recognition Vol. 79, 260-278, 2018.
[22]
D. S. Alexiadis,; A. Chatzitofis,; N. Zioulis,; O. Zoidi,; G. Louizis,; D. Zarpalas,; P. Daras, An integrated platform for live 3D human reconstruction and motion capturing. IEEE Transactions on Circuits and Systems for Video Technology Vol. 27, No. 4, 798-813, 2017.
[23]
C. Kuster,; J.-C. Bazin,; C. Öztireli,; T. Deng,; T. Martin,; T. Popa,; M. Gross, Spatio-temporal geometry fusion for multiple hybrid cameras using moving least squares surfaces. Computer Graphics Forum Vol. 33, No. 2, 1-10, 2014.
[24]
S. Meerits,; V. Nozick,; H. Saito, Real-time scene reconstruction and triangle mesh generation using multiple RGB-D cameras. Journal of Real-Time Image Processing , 2017.
[25]
G. Turk,; M. Levoy, Zippered polygon meshes from range images. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, 311-318, 1994.
[26]
Z. Yan,; X. Xiang, Scene flow estimation: A survey. arXiv preprint arXiv:1612.02590, 2016.
[27]
L. Li,; S. Xiang,; Y. Yang,; L. Yu, Multi-camera interference cancellation of time-of-flight (TOF) cameras. In: Proceedings of the IEEE International Conference on Image Processing, 556-560, 2015.
[28]
G. Guennebaud,; M. Gross, Algebraic point set surfaces. ACM Transactions on Graphics Vol. 26, No. 3, Article No. 23, 2007.
[29]
J. Chen,; D. Bautembach,; S. Izadi, Scalable real-time volumetric surface reconstruction. ACM Transactions on Graphics Vol. 32, No. 4, Article No. 113, 2013.
[30]
F. Steinbrücker,; J. Sturm,; D. Cremers, Volumetric 3D mapping in real-time on a CPU. In: Proceedings of the IEEE International Conference on Robotics and Automation, 2021-2028, 2014.
[31]
M. Alexa,; A. Adamson, On normals and projection operators for surfaces defined by point sets. In: Proceedings of the First Eurographics Conference on Point-Based Graphics, 149-155, 2004.
[32]
W. E. Lorensen,; H. E. Cline, Marching cubes: A high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics Vol. 21, No. 4, 163-169, 1987.
[33]
C. Everitt, Interactive order-independent transparency. White paper, nVIDIA Vol. 2, No. 6, 7, 2001.
Computational Visual Media
Pages 287-303
Cite this article:
Meerits S, Thomas D, Nozick V, et al. FusionMLS: Highly dynamic 3D reconstruction with consumer-grade RGB-D cameras. Computational Visual Media, 2018, 4(4): 287-303. https://doi.org/10.1007/s41095-018-0121-0

802

Views

30

Downloads

12

Crossref

N/A

Web of Science

13

Scopus

3

CSCD

Altmetrics

Revised: 09 March 2018
Accepted: 29 May 2018
Published: 22 August 2018
© The Author(s) 2018

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.

Return