18.6.2 Motion, Shape from Motion for RGB-D Sensors, Kinect Motion

Chapter Contents (Back)
RGB-D. Motion and Depth.

Yang, J.Y.[Jing-Yu], Ye, X.C.[Xin-Chen], Li, K.[Kun], Hou, C.P.[Chun-Ping], Wang, Y.[Yao],
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model,
IP(23), No. 8, August 2014, pp. 3443-3458.
IEEE DOI 1408
BibRef
Earlier: A1, A2, A3, A4, Only:
Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model,
ECCV12(V: 158-171).
Springer DOI 1210
autoregressive processes. from low quality ToF cameras BibRef

Li, K.[Kun], Zhu, Y.M.[Yan-Ming], Yang, J.Y.[Jing-Yu], Jiang, J.M.[Jian-Min],
Video super-resolution using an adaptive superpixel-guided auto-regressive model,
PR(51), No. 1, 2016, pp. 59-71.
Elsevier DOI 1601
Video super-resolution BibRef

Hadfield, S.[Simon], Bowden, R.[Richard],
Scene Particles: Unregularized Particle-Based Scene Flow Estimation,
PAMI(36), No. 3, March 2014, pp. 564-576.
IEEE DOI 1403
BibRef
And:
Scene Flow Estimation using Intelligent Cost Functions,
BMVC14(xx-yy).
HTML Version. 1410
BibRef
Earlier:
Go with the Flow: Hand Trajectories in 3D via Clustered Scene Flow,
ICIAR12(I: 285-295).
Springer DOI 1206
BibRef
Earlier:
Kinecting the dots: Particle based scene flow from depth sensors,
ICCV11(2290-2295).
IEEE DOI 1201
image sequences. 3D flow, not just 2-D optic flow. With Kinect sensor. BibRef

Lebeda, K.[Karel], Hadfield, S.[Simon], Bowden, R.[Richard],
Direct-from-Video: Unsupervised NRSfM,
6DPose16(III: 578-594).
Springer DOI 1611
BibRef
And:
Dense Rigid Reconstruction from Unstructured Discontinuous Video,
3DRR15(814-822)
IEEE DOI 1602
BibRef
Earlier:
2D or Not 2D: Bridging the Gap Between Tracking and Structure from Motion,
ACCV14(IV: 642-658).
Springer DOI 1504
BibRef

Wang, K., Zhang, G.F.[Guo-Feng], Bao, H.J.[Hu-Jun],
Robust 3D Reconstruction With an RGB-D Camera,
IP(23), No. 11, November 2014, pp. 4893-4906.
IEEE DOI 1410
Cameras BibRef

Zhang, G.F.[Guo-Feng], Jia, J.Y.[Jia-Ya], Bao, H.J.[Hu-Jun],
Simultaneous multi-body stereo and segmentation,
ICCV11(826-833).
IEEE DOI 1201
Multi-view stereo. Dense depth map. Segmentation from monocular image sequence. BibRef

Xie, Q., Remil, O., Guo, Y., Wang, M., Wei, M., Wang, J.,
Object Detection and Tracking Under Occlusion for Object-Level RGB-D Video Segmentation,
MultMed(20), No. 3, March 2018, pp. 580-592.
IEEE DOI 1802
Algorithm design and analysis, Clustering algorithms, Image segmentation, Motion segmentation, Object segmentation, spatio-temporal segmentation BibRef


Bai, J., Yang, J., Ye, X., Hou, C.,
Depth refinement for binocular kinect RGB-D cameras,
VCIP16(1-4)
IEEE DOI 1701
Cameras BibRef

Cheng, S.C.[Shyi-Chyi], Su, J.Y.[Jui-Yuan], Chen, J.M.[Jing-Min], Hsieh, J.W.[Jun-Wei],
Model-Based 3D Scene Reconstruction Using a Moving RGB-D Camera,
MMMod17(I: 214-225).
Springer DOI 1701
BibRef

Kam, H.C.[Ho Chuen], Wong, K.H.[Kin Hong], Zhang, B.[Baiwu],
Dual Back-to-Back Kinects for 3-D Reconstruction,
ISVC16(I: 858-867).
Springer DOI 1701
rgb-d BibRef

Kehl, W.[Wadim], Navab, N.[Nassir], Ilic, S.[Slobodan],
Coloured signed distance fields for full 3D object reconstruction,
BMVC14(xx-yy).
HTML Version. 1410
geometry of an object using an RGB-D device. depth and color and motion. BibRef

Xiao, J.X.[Jian-Xiong], Owens, A.[Andrew], Torralba, A.[Antonio],
SUN3D: A Database of Big Spaces Reconstructed Using SfM and Object Labels,
ICCV13(1625-1632)
IEEE DOI 1403
Dataset, Scene Understanding.
WWW Link. RGB-D Video dataset. Camera pose and object labels. Interactive reconstruction process. BibRef

da Silva Pires, D.[David], Cesar, Jr., R.M.[Roberto M.], Velho, L.[Luiz],
Motion Estimation from RGB-D Images Using Graph Homomorphism,
CIARP13(II:487-494).
Springer DOI 1311
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Spatio-Temporal Analysis -- Many Frames .


Last update:Nov 12, 2018 at 11:26:54