research-article

Real-time High-accuracy Three-Dimensional Reconstruction with Consumer RGB-D Cameras

Authors:

Shi-Min HuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 37, Issue 5

Article No.: 171, Pages 1 - 16

https://doi.org/10.1145/3182157

Published: 14 September 2018 Publication History

Abstract

We present an integrated approach for reconstructing high-fidelity three-dimensional (3D) models using consumer RGB-D cameras. RGB-D registration and reconstruction algorithms are prone to errors from scanning noise, making it hard to perform 3D reconstruction accurately. The key idea of our method is to assign a probabilistic uncertainty model to each depth measurement, which then guides the scan alignment and depth fusion. This allows us to effectively handle inherent noise and distortion in depth maps while keeping the overall scan registration procedure under the iterative closest point framework for simplicity and efficiency. We further introduce a local-to-global, submap-based, and uncertainty-aware global pose optimization scheme to improve scalability and guarantee global model consistency. Finally, we have implemented the proposed algorithm on the GPU, achieving real-time 3D scanning frame rates and updating the reconstructed model on-the-fly. Experimental results on simulated and real-world data demonstrate that the proposed method outperforms state-of-the-art systems in terms of the accuracy of both recovered camera trajectories and reconstructed models.

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References

[1]

P. J. Besl and N. D. McKay. 1992. A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 2 (Feb. 1992), 239--256.

Digital Library

[2]

S. Bouaziz, A. Tagliasacchi, and M. Pauly. 2013. Sparse iterative closest point. Comput. Graph. Forum 32, 5 (2013), 1--11.

Digital Library

[3]

J. Chen, D. Bautembach, and S. Izadi. 2013. Scalable real-time volumetric surface reconstruction. ACM Trans. Graph. 32, 4, Article 113 (Jul. 2013), 16 pages.

Digital Library

[4]

S. Choi, Q. Y. Zhou, and V. Koltun. 2015. Robust reconstruction of indoor scenes. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 5556--5565.

[5]

Y. Cui, S. Schuon, S. Thrun, D. Stricker, and C. Theobalt. 2013. Algorithms for 3d shape scanning with a depth camera. IEEE Trans. Pattern Anal. Mach. Intell. 35, 5 (2013), 1039--1050.

Digital Library

[6]

B. Curless and M. Levoy. 1996. A volumetric method for building complex models from range images. In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’96). ACM, New York, NY, 303--312.

Digital Library

[7]

A. Dai, M. Nießner, M. Zollhöfer, S. Izadi, and C. Theobalt. 2017. BundleFusion: Real-time globally consistent 3D reconstruction using on-the-fly surface reintegration. ACM Trans. Graph. 36, 3, Article 24 (May 2017), 18 pages.

Digital Library

[8]

M. Danelljan, G. Meneghetti, F. S. Khan, and M. Felsberg. 2016. A probabilistic framework for color-based point set registration. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 1818--1826.

[9]

J. R. Diebel, S. Thrun, and M. Brünig. 2006. A Bayesian method for probable surface reconstruction and decimation. ACM Trans. Graph. 25, 1 (Jan. 2006), 39--59.

Digital Library

[10]

F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, and W. Burgard. 2012. An evaluation of the RGB-D SLAM system. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation. 1691--1696.

[11]

F. Endres, J. Hess, J. Sturm, D. Cremers, and W. Burgard. 2014. 3-D mapping with an RGB-D camera. IEEE Trans. Robot. 30, 1 (Feb 2014), 177--187.

Digital Library

[12]

J. Engel, T. Schöps, and D. Cremers. 2014. LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the 13th European Conference on Computer Vision (ECCV’14), David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars (Eds.). Springer International Publishing, Cham, 834--849.

[13]

D. Ferstl, C. Reinbacher, G. Riegler, M. Rüther, and H. Bischof. 2015. Learning depth calibration of time-of-flight cameras. In Proceedings of the British Machine Vision Conference (BMVC’15), Mark W. Jones Xianghua Xie and Gary K. L. Tam (Eds.). BMVA Press, Article 102, 12 pages.

[14]

N. Fioraio, J. Taylor, A. Fitzgibbon, L. Di Stefano, and S. Izadi. 2015. Large-scale and drift-free surface reconstruction using online subvolume registration. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’15). 4475--4483.

[15]

S. Fuhrmann and M. Goesele. 2014. Floating scale surface reconstruction. ACM Trans. Graph. 33, 4, Article 46 (July 2014), 11 pages.

Digital Library

[16]

D. Gálvez-López and J. D. Tardos. 2012. Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28, 5 (Oct. 2012), 1188--1197.

Digital Library

[17]

B. Glocker, J. Shotton, A. Criminisi, and S. Izadi. 2015. Real-time RGB-D camera relocalization via randomized ferns for keyframe encoding. IEEE Trans. Vis. Comput. Graph. 21, 5 (May 2015), 571--583.

[18]

G. Grigoryan and P. Rheingans. 2004. Point-based probabilistic surfaces to show surface uncertainty. IEEE Trans. Vis. Comput. Graph. 10, 5 (Sept 2004), 564--573.

Digital Library

[19]

A. Handa, T. Whelan, J. McDonald, and A. J. Davison. 2014. A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA’14). 1524--1531.

[20]

P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox. 2012. RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments. Int. J. Rob. Res. 31, 5 (April 2012), 647--663.

Digital Library

[21]

D. Holz, A. E. Ichim, F. Tombari, R. B. Rusu, and S. Behnke. 2015. Registration with the point cloud library: A modular framework for aligning in 3-D. IEEE Robot. Automat. Mag. 22, 4 (Dec 2015), 110--124.

[22]

P. J. Huber. 2011. Robust Statistics. Springer.

[23]

P. Jenke, M. Wand, M. Bokeloh, A. Schilling, and W. Strasser. 2006. Bayesian point cloud reconstruction. Comput. Graph. Forum 25, 3 (2006), 379--388.

[24]

B. Jian and B. C. Vemuri. 2011. Robust point set registration using Gaussian mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 33, 8 (Aug 2011), 1633--1645.

Digital Library

[25]

A. Jordt and R. Koch. 2013. Reconstruction of Deformation from Depth and Color Video with Explicit Noise Models. Springer, Berlin, 128--146.

[26]

O. Kähler, V. A. Prisacariu, and D. W. Murray. 2016. Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure. Springer International Publishing, Cham, 500--516.

[27]

O. Kähler, V. A. Prisacariu, C. Y. Ren, X. Sun, P. Torr, and D. Murray. 2015. Very high frame rate volumetric integration of depth images on mobile devices. IEEE Trans. Vis. Comput. Graph. 21, 11 (Nov 2015), 1241--1250.

Digital Library

[28]

A. Kalaiah and A. Varshney. 2003. Statistical point geometry. In Proceedings of the 2003 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing (SGP’03). 107--115. http://dl.acm.org/citation.cfm?id=882370.882385

Digital Library

[29]

M. Keller, D. Lefloch, M. Lambers, S. Izadi, T. Weyrich, and A. Kolb. 2013. Real-time 3D reconstruction in dynamic scenes using point-based fusion. In Proceedings of the 2013 International Conference on 3D Vision (3DV’13). 1--8.

Digital Library

[30]

C. Kerl, J. Sturm, and D. Cremers. 2013. Dense visual SLAM for RGB-D cameras. In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2100--2106.

[31]

Y. M. Kim, D. Chan, C. Theobalt, and S. Thrun. 2008. Design and calibration of a multi-view TOF sensor fusion system. In Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 1--7.

[32]

R. Kolluri, J. Richard Shewchuk, and J. F. O’Brien. 2004. Spectral surface reconstruction from noisy point clouds. In Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing (SGP’04). ACM, New York, NY, USA, 11--21.

Digital Library

[33]

R. Kümmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard. 2011. G2o: A general framework for graph optimization. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation. 3607--3613.

[34]

F. Lenzen, K. I. Kim, H. Schäfer, R. Nair, S. Meister, F. Becker, C. S. Garbe, and C. Theobalt. 2013. Denoising Strategies for Time-of-Flight Data. Springer, Berlin, 25--45.

[35]

H. Li, E. Vouga, A. Gudym, L. Luo, J. T. Barron, and G. Gusev. 2013. 3D self-portraits. ACM Trans. Graph. 32, 6, Article 187 (Nov. 2013), 9 pages.

Digital Library

[36]

R. Maier, J. Sturm, and D. Cremers. 2014. Submap-based bundle adjustment for 3D reconstruction from RGB-D data. In German Conference on Pattern Recognition. Springer, 54--65.

[37]

N. Mellado, D. Aiger, and N. J. Mitra. 2014. Super 4PCS fast global pointcloud registration via smart indexing. Comput. Graph. Forum 33, 5 (2014), 205--215.

Digital Library

[38]

R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós. 2015. ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31, 5 (Oct. 2015), 1147--1163.

Digital Library

[39]

A. Myronenko and X. Song. 2010. Point set registration: Coherent point drift. IEEE Trans. Pattern Anal. Mach. Intell. 32, 12 (Dec. 2010), 2262--2275.

Digital Library

[40]

R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon. 2011. KinectFusion: Real-time dense surface mapping and tracking. In Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality. 127--136.

Digital Library

[41]

C. V. Nguyen, S. Izadi, and D. Lovell. 2012. Modeling Kinect sensor noise for improved 3D reconstruction and tracking. In Proceedings of the 2012 2nd International Conference on 3D Imaging, Modeling, Processing, Visualization Transmission. 524--530.

Digital Library

[42]

M. Nießner, M. Zollhöfer, S. Izadi, and M. Stamminger. 2013. Real-time 3D reconstruction at scale using voxel hashing. ACM Trans. Graph. 32, 6, Article 169 (Nov. 2013), 11 pages.

Digital Library

[43]

E. Olson and P. Agarwal. 2013. Inference on networks of mixtures for robust robot mapping. Int. J. Rob. Res. 32, 7 (June 2013), 826--840.

Digital Library

[44]

J. H. Park, Y. D. Shin, J. H Bae, and M. H. Baeg. 2012. Spatial uncertainty model for visual features using a Kinect sensor. Sensors 12, 7 (2012), 8640--8662.

[45]

R. R. Paulsen, J. A. Baerentzen, and R. Larsen. 2010. Markov random field surface reconstruction. IEEE Trans. Vis. Comput. Graph. 16, 4 (July 2010), 636--646.

Digital Library

[46]

F. Reichl, J. Weiss, and R. Westermann. 2016. Memory-efficient interactive online reconstruction from depth image streams. In Computer Graphics Forum 35, 8 (2016), 108--119.

[47]

M. Reynolds, J. Doboš, L. Peel, T. Weyrich, and G. J. Brostow. 2011. Capturing time-of-flight data with confidence. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’11). 945--952.

Digital Library

[48]

H. Roth and M. Vona. 2012. Moving volume KinectFusion. In Proceedings of the British Machine Vision Conference. British Machine Vision Association, 112.1--112.11.

[49]

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski. 2011. ORB: An efficient alternative to SIFT or SURF. In Proceedings of the 2011 International Conference on Computer Vision. 2564--2571.

Digital Library

[50]

M. Ruhnke, R. Kümmerle, G. Grisetti, and W. Burgard. 2012. Highly accurate 3D surface models by sparse surface adjustment. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation. 751--757.

[51]

S. Rusinkiewicz and M. Levoy. 2001. Efficient variants of the ICP algorithm. In Proceedings of the 3rd International Conference on 3-D Digital Imaging and Modeling. 145--152.

[52]

A. Segal, D. Haehnel, and S. Thrun. 2009. Generalized-ICP. In Robotics: Science and Systems, Vol. 2.

[53]

F. Steinbrücker, C. Kerl, and D. Cremers. 2013. Large-scale multi-resolution surface reconstruction from RGB-D sequences. In Proceedings of the 2013 IEEE International Conference on Computer Vision. 3264--3271.

Digital Library

[54]

J. Stückler and S. Behnke. 2014. Multi-resolution surfel maps for efficient dense 3D modeling and tracking. J. Vis. Commun. Image Represent. 25, 1 (2014), 137--147.

Digital Library

[55]

J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers. 2012. A benchmark for the evaluation of RGB-D SLAM systems. In Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. 573--580.

[56]

A. Teichman, S. Miller, and S. Thrun. 2013. Unsupervised intrinsic calibration of depth sensors via SLAM. In Robotics: Science and Systems, Vol. 248.

[57]

C. Wang and X. Guo. 2017. Feature-based RGB-D camera pose optimization for real-time 3D reconstruction. Comput. Vis. Media 3, 2 (2017), 95--106.

[58]

H. Wang, J. Wang, and L. Wang. 2016. Online reconstruction of indoor scenes from RGB-D streams. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 3271--3279.

[59]

O. Wasenmüller, M. Meyer, and D. Stricker. 2016. CoRBS: Comprehensive RGB-D benchmark for SLAM using Kinect v2. In Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV’16). 1--7.

[60]

T. Whelan, M. Kaess, H. Johannsson, M. Fallon, J. J. Leonard, and J. Mcdonald. 2015a. Real-time large-scale dense RGB-D SLAM with volumetric fusion. Int. J. Rob. Res. 34, 4--5 (April 2015), 598--626.

Digital Library

[61]

T. Whelan, S. Leutenegger, R. F. Salas-Moreno, B. Glocker, and A. J. Davison. 2015b. ElasticFusion: Dense SLAM without a pose graph. Robotics: Science and Systems (2015).

[62]

H. Yamazoe, H. Habe, I. Mitsugami, and Y. Yagi. 2018. Depth error correction for projector-camera based consumer depth cameras. Comput. Vis. Media 4, 2 (2018), 1--9.

[63]

J. Yang, H. Li, and Y. Jia. 2013. Go-ICP: Solving 3D registration efficiently and globally optimally. In Proceedings of the 2013 IEEE International Conference on Computer Vision. 1457--1464.

Digital Library

[64]

A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser. 2017. 3DMatch: Learning local geometric descriptors from RGB-D reconstructions. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'17). 199--208.

[65]

M. Zeng, F. Zhao, J. Zheng, and X. Liu. 2013. Octree-based fusion for realtime 3D reconstruction. Graph. Models 75, 3 (May 2013), 126--136.

Digital Library

[66]

Q. Y. Zhou, S. Miller, and V. Koltun. 2013. Elastic fragments for dense scene reconstruction. In Proceedings of the 2013 IEEE International Conference on Computer Vision. 473--480.

Digital Library

[67]

Q. Y. Zhou, J. Park, and V. Koltun. 2016. Fast Global Registration. Springer International Publishing, Cham, 766--782.

[68]

M. Zollhöfer, A. Dai, M. Innmann, C. Wu, M. Stamminger, C. Theobalt, and M. Nießner. 2015. Shading-based refinement on volumetric signed distance functions. ACM Trans. Graph. 34, 4, Article 96 (July 2015), 14 pages.

Digital Library

Cited By

Hu HSong A(2025)Digital image correlation calculation method for RGB-D camera multi-view matching using variable templateMeasurement10.1016/j.measurement.2024.115617240(115617)Online publication date: Jan-2025
https://doi.org/10.1016/j.measurement.2024.115617
Peng ZShao TLiu YZhou JYang YWang JZhou K(2024)RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian SplattingACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657455(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657455
Chen JZhang J(2024)Depth Image Inpainting Algorithm Based on Improved Non-Local Means Filtering2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603656(964-967)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603656
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Index Terms

Real-time High-accuracy Three-Dimensional Reconstruction with Consumer RGB-D Cameras
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 37, Issue 5

October 2018

140 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3278329

Editor:
Marc Alexa
TU Berlin

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Publication History

Published: 14 September 2018

Accepted: 01 May 2018

Revised: 01 April 2018

Received: 01 October 2017

Published in TOG Volume 37, Issue 5

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Joint NSFC-DFG Research Program
German Research Foundation, DFG
European Research Council, ERC Advanced Grant ACROSS
Natural Science Foundation of China

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https://doi.org/10.1016/j.measurement.2024.115617
Peng ZShao TLiu YZhou JYang YWang JZhou K(2024)RTG-SLAM: Real-time 3D Reconstruction at Scale using Gaussian SplattingACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657455(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657455
Chen JZhang J(2024)Depth Image Inpainting Algorithm Based on Improved Non-Local Means Filtering2024 5th International Conference on Computer Engineering and Application (ICCEA)10.1109/ICCEA62105.2024.10603656(964-967)Online publication date: 12-Apr-2024
https://doi.org/10.1109/ICCEA62105.2024.10603656
Liso LSandström EYugay VVan Gool LOswald M(2024)Loopy-SLAM: Dense Neural SLAM with Loop Closures2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01925(20363-20373)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01925
Zhu JGao CSun QWang MDeng Z(2024)A Survey of Indoor 3D Reconstruction Based on RGB-D CamerasIEEE Access10.1109/ACCESS.2024.344306512(112742-112766)Online publication date: 2024
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Huang SChen HHuang JFu HHu S(2023)Real-Time Globally Consistent 3D Reconstruction With Semantic PriorsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.313791229:4(1977-1991)Online publication date: 1-Apr-2023
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