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High-speed Depth Stream Generation from a Hybrid Camera

Published: 01 October 2016 Publication History

Abstract

High-speed video has been commonly adopted in consumer-grade cameras, augmenting these videos with a corresponding depth stream will enable new multimedia applications, such as 3D slow-motion video. In this paper, we present a hybrid camera system that combines a high-speed color camera with a depth sensor, e.g. Kinect depth sensor, to generate a depth stream that can produce both high-speed and high-resolution RGB+depth stream. Simply interpolating the low-speed depth frames is not satisfactory, where interpolation artifacts and lose in surface details are often visible. We have developed a novel framework that utilizes both shading constraints within each frame and optical flow constraints between neighboring frames. More specifically we present (a) an effective method to find the intrinsics images to allow more accurate normal estimation; and (b) an optimization-based framework to estimate the high-resolution/high-speed depth stream, taking into consideration temporal smoothness and shading/depth consistency. We evaluated our holistic framework with both synthetic and real sequences, it showed superior performance than previous state-of-the-art.

References

[1]
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI, 34(11):2274--2282, 2012.
[2]
O. M. Aodha, N. D. Campbell, A. Nair, and G. J. Brostow. Patch based synthesis for single depth image super-resolution. In ECCV, pages 71--84, 2012.
[3]
J. T. Barron and J. Malik. High-frequency shape and albedo from shading using natural image statistics. In CVPR, pages 2521--2528, 2011.
[4]
J. T. Barron and J. Malik. Shape, illumination, and reflectance from shading. IEEE TPAMI, 37(8):1670--1687, 2015.
[5]
J. T. Barron and J. Malik. Intrinsic scene properties from a single rgb-d image. IEEE TPAMI, 38(4):690--703, 2016.
[6]
H. G. Barrow and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. Comput. Vis. Syst., 1978.
[7]
S. Bi, X. Han, and Y. Yu. An l1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition. ACM Trans. Graph., 34(4):78, 2015.
[8]
N. Bonneel, K. Sunkavalli, J. Tompkin, D. Sun, S. Paris, and H. Pfister. Interactive intrinsic video editing. ACM Trans. Graph., 33(6):197, 2014.
[9]
A. Bousseau, S. Paris, and F. Durand. User-assisted intrinsic images. ACM Trans. Graph., 28(5):130, 2009.
[10]
T. Brox and J. Malik. Large displacement optical flow: descriptor matching in variational motion estimation. IEEE TPAMI, 33(3):500--513, 2011.
[11]
Q. Chen and V. Koltun. A simple model for intrinsic image decomposition with depth cues. In ICCV, pages 241--248, 2013.
[12]
J. Durou, M. Falcone, and M. Sagona. Numerical methods for shape-from-shading: A new survey with benchmarks. CVIU, 109(1):22--43, 2008.
[13]
D. Ferstl, C. Reinbacher, R. Ranftl, M. Rüther, and H. Bischof. Image guided depth upsampling using anisotropic total generalized variation. In ICCV, pages 993--1000, 2013.
[14]
E. Garces, A. Munoz, J. Lopez-Moreno, and D. D. Gutierrez. Intrinsic images by clustering. In Computer Graphics Forum, volume 31, pages 1415--1424, 2012.
[15]
Y. Han, J. Lee, and I. Kweon. High quality shape from a single rgb-d image under uncalibrated natural illumination. In ICCV, pages 1617--1624, 2013.
[16]
S. M. Haque, A. Chatterjee, and V. M. Govindu. High quality photometric reconstruction using a depth camera. In CVPR, pages 2283--2290, 2014.
[17]
E. Herbst, X. Ren, and D. Fox. Rgb-d flow: Dense 3-d motion estimation using color and depth. In ICRA, pages 2276--2282, 2013.
[18]
B. K. P. Horn. Shape from Shading: A Method for Obtaining the Shape of a Smooth Opaque Object from One View. PhD thesis, MIT, 1970.
[19]
J. Jeon, S. Cho, X. Tong, and S. Lee. Intrinsic image decomposition using structure-texture separation and surface normals. In ECCV, pages 218--233, 2014.
[20]
N. Kong, P. Gehler, and M. Black. Intrinsic video. In ECCV, pages 360--375. 2014.
[21]
P. Laffont, A. Bousseau, S. Paris, F. Durand, and G. Drettakis. Coherent intrinsic images from photo collections. ACM Trans. Graph., 31(6), 2012.
[22]
E. H. Land and J. J. McCann. Lightness and retinex theory. JOSA, 1971.
[23]
K. J. Lee, Q. Zhao, X. Tong, M. Gong, S. Izadi, S. U. Lee, P. Tan, and S. Lin. Estimation of intrinsic image sequences from image
[24]
depth video. In ECCV, pages 327--340. 2012.
[25]
R. Or-El, G. Rosman, A. Wetzler, R. Kimmel, and A. M. Bruckstein. Rgbd-fusion: Real-time high precision depth recovery. In CVPR, pages 5407--5416, 2015.
[26]
J. Park, H. Kim, Y. Tai, M. S. Brown, and I. Kweon. High quality depth map upsampling for 3d-tof cameras. In ICCV, pages 1623--1630, 2011.
[27]
R. Ramamoorthi and P. Hanrahan. An efficient representation for irradiance environment maps. In ACM SIGGRAPH, pages 497--500, 2001.
[28]
C. Richardt, C. Stoll, N. A. Dodgson, H. Seidel, and C. Theobalt. Coherent spatiotemporal filtering, upsampling and rendering of rgbz videos. In Computer Graphics Forum, volume 31, pages 247--256, 2012.
[29]
J. Shi, Y. Dong, X. Tong, and Y. Chen. Efficient intrinsic image decomposition for rgbd images. In ACM VRST, pages 17--25, 2015.
[30]
Y. Tai, H. Du, M. S. Brown, and L. Stephen. Correction of spatially varying image and video motion blur using a hybrid camera. IEEE TPAMI, 32(6):1012--1028, 2010.
[31]
M. F. Tappen, W. T. Freeman, and E. H. Adelson. Recovering intrinsic images from a single image. IEEE TPAMI, 27(9):1459--1472, 2005.
[32]
C. Wu, K. Varanasi, Y. Liu, H. Seidel, and C. Theobalt. Shading-based dynamic shape refinement from multi-view video under general illumination. In ICCV, pages 1108--1115, 2011.
[33]
C. Wu, M. Zollhöfer, M. Nießner, M. Stamminger, S. Izadi, and C. Theobalt. Real-time shading-based refinement for consumer depth cameras. ACM Trans. Graph., 33(3), 2014.
[34]
Q. Yang, R. Yang, J. Davis, and D. Nistér. Spatial-depth super resolution for range images. In CVPR, 2007.
[35]
G. Ye, E. Garces, Y. Liu, Q. Dai, and D. Gutierrez. Intrinsic video and applications. ACM Trans. Graph., 33(4):80, 2014.
[36]
L. Yu, S. Yeung, Y. Tai, and S. Lin. Shading-based shape refinement of rgb-d images. In CVPR, pages 1415--1422, 2013.
[37]
Q. Zhang, M. Ye, R. Yang, Y. Matsushita, B. Wilburn, and H. Yu. Edge-preserving photometric stereo via depth fusion. In CVPR, pages 2472--2479, 2012.
[38]
Q. Zhao, P. Tan, Q. Dai, L. Shen, E. Wu, and S. Lin. A closed-form solution to retinex with nonlocal texture constraints. IEEE TPAMI, 34(7):1437--1444, 2012.
[39]
J. Zhu, L. Wang, R. Yang, J. Davis, and Z. Pan. Reliability fusion of time-of-flight depth and stereo geometry for high quality depth maps. IEEE TPAMI, 33(7):1400--1414, 2011.

Cited By

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  • (2021)Automatic layered RGB‐D scene flow estimation with optical flow field constraintIET Image Processing10.1049/iet-ipr.2020.023014:16(4092-4101)Online publication date: 23-Feb-2021
  • (2019)Real-Time Artifact Compensation for Depth Images of Multi-Frequency ToFXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University10.1051/jnwpu/2019371015237:1(152-159)Online publication date: 3-Apr-2019

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cover image ACM Conferences
MM '16: Proceedings of the 24th ACM international conference on Multimedia
October 2016
1542 pages
ISBN:9781450336031
DOI:10.1145/2964284
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 October 2016

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Author Tags

  1. depth stream
  2. high-speed imaging
  3. intrinisic decomposition
  4. shape from shading

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MM '16
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MM '16: ACM Multimedia Conference
October 15 - 19, 2016
Amsterdam, The Netherlands

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MM '16 Paper Acceptance Rate 52 of 237 submissions, 22%;
Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

View all
  • (2021)Automatic layered RGB‐D scene flow estimation with optical flow field constraintIET Image Processing10.1049/iet-ipr.2020.023014:16(4092-4101)Online publication date: 23-Feb-2021
  • (2019)Real-Time Artifact Compensation for Depth Images of Multi-Frequency ToFXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University10.1051/jnwpu/2019371015237:1(152-159)Online publication date: 3-Apr-2019

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