Gong, M.L.[Ming-Lun],
Real-time joint disparity and disparity flow estimation on programmable
graphics hardware,
CVIU(113), No. 1, January 2009, pp. 90-100.
Elsevier DOI
0812
Stereo vision; Motion estimation; 3D Scene flow; Disparity flow
BibRef
Basha, T.[Tali],
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Multi-view Scene Flow Estimation: A View Centered Variational Approach,
IJCV(101), No. 1, January 2013, pp. 6-21.
WWW Link.
1302
BibRef
Earlier:
CVPR10(1506-1513).
IEEE DOI
1006
BibRef
Popham, T.[Thomas],
Bhalerao, A.[Abhir],
Wilson, R.G.[Roland G.],
Estimating scene flow using an interconnected patch surface model
with belief-propagation inference,
CVIU(121), No. 1, 2014, pp. 74-85.
Elsevier DOI
1404
Motion
BibRef
Bakkay, M.C.[Mohamed Chafik],
Zagrouba, E.[Ezzeddine],
Spatio-temporal filter for dense real-time Scene Flow estimation of
dynamic environments using a moving RGB-D camera,
PRL(59), No. 1, 2015, pp. 33-40.
Elsevier DOI
1505
Scene Flow
BibRef
Wang, Y.C.[Yu-Cheng],
Zhang, J.[Jian],
Liu, Z.C.[Zi-Cheng],
Wu, Q.A.[Qi-Ang],
Chou, P.A.[Philip A.],
Zhang, Z.Y.[Zheng-You],
Jia, Y.D.[Yun-De],
Handling Occlusion and Large Displacement Through Improved RGB-D
Scene Flow Estimation,
CirSysVideo(26), No. 7, July 2016, pp. 1265-1278.
IEEE DOI
1608
BibRef
Earlier:
Completed Dense Scene Flow in RGB-D Space,
BD3DCV14(191-205).
Springer DOI
1504
computational complexity
BibRef
Zou, C.[Cheng],
He, B.[Bingwei],
Zhang, L.W.[Li-Wei],
Zhang, J.W.[Jian-Wei],
Scene flow for 3D laser scanner and camera system,
IET-IPR(12), No. 4, April 2018, pp. 612-618.
DOI Link
1804
BibRef
Zou, C.[Cheng],
He, B.W.[Bing-Wei],
Zhu, M.Z.[Ming-Zhu],
Zhang, L.W.[Li-Wei],
Zhang, J.W.[Jian-Wei],
Learning motion field of LiDAR point cloud with convolutional
networks,
PRL(125), 2019, pp. 514-520.
Elsevier DOI
1909
Motion field, CNNs, LiDAR
BibRef
Zou, C.[Cheng],
He, B.[Bingwei],
Zhang, L.[Liwei],
Zhang, J.W.[Jian-Wei],
Static map reconstruction and dynamic object tracking for a camera and
laser scanner system,
IET-CV(12), No. 4, June 2018, pp. 384-392.
DOI Link
1805
BibRef
Lv, Z.Y.[Zhao-Yang],
Kim, K.[Kihwan],
Troccoli, A.[Alejandro],
Sun, D.Q.[De-Qing],
Rehg, J.M.[James M.],
Kautz, J.[Jan],
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion
Field Estimation,
ECCV18(VI: 484-501).
Springer DOI
1810
BibRef
Navarro, J.[Julia],
Buades, A.[Antoni],
Semi-dense and robust image registration by shift adapted weighted
aggregation and variational completion,
IVC(89), 2019, pp. 258-275.
Elsevier DOI
1909
Image correspondences, Stereo, Optical flow, Block-matching, Interpolation
BibRef
Navarro, J.,
Garamendi, J.F.,
Variational scene flow and occlusion detection from a light field
sequence,
WSSIP16(1-4)
IEEE DOI
1608
cameras
BibRef
Zou, C.[Cheng],
He, B.W.[Bing-Wei],
Zhu, M.Z.[Ming-Zhu],
Zhang, L.W.[Li-Wei],
Zhang, J.W.[Jian-Wei],
Scene flow estimation by depth map upsampling and layer assignment
for camera-LiDAR system,
JVCIR(64), 2019, pp. 102616.
Elsevier DOI
1911
3D scene flow, Sensor fusion, Depth map upsampling
BibRef
Schuster, R.[René],
Wasenmüller, O.[Oliver],
Unger, C.[Christian],
Kuschk, G.[Georg],
Stricker, D.[Didier],
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and
Robust Interpolation for Scene Flow Estimation,
IJCV(128), No. 2, February 2020, pp. 527-546.
Springer DOI
2002
BibRef
Ma, S.[Sizhuo],
Smith, B.M.[Brandon M.],
Gupta, M.[Mohit],
Differential Scene Flow from Light Field Gradients,
IJCV(128), No. 3, March 2020, pp. 679-697.
Springer DOI
2003
BibRef
Earlier:
3D Scene Flow from 4D Light Field Gradients,
ECCV18(VIII: 681-698).
Springer DOI
1810
BibRef
Liu, J.J.[Jia-Jie],
Li, H.[Han],
Wu, R.H.[Rui-Hong],
Zhao, Q.Y.[Qing-Yun],
Guo, Y.Y.[Yi-You],
Chen, L.[Long],
A survey on deep learning methods for scene flow estimation,
PR(106), 2020, pp. 107378.
Elsevier DOI
2006
Scene flow, Optical flow, Depth estimation, Deep learning
BibRef
Li, Q.[Qing],
Wang, C.[Cheng],
Li, X.[Xin],
Wen, C.L.[Cheng-Lu],
FeatFlow: Learning geometric features for 3D motion estimation,
PR(111), 2021, pp. 107574.
Elsevier DOI
2012
Feature learning, Motion estimation, Point clouds, Scene flow,
Scan-matching, Ego-motion
BibRef
Zhou, G.,
Bao, X.,
Ye, S.,
Wang, H.,
Yan, H.,
Selection of Optimal Building Facade Texture Images From UAV-Based
Multiple Oblique Image Flows,
GeoRS(59), No. 2, February 2021, pp. 1534-1552.
IEEE DOI
2101
Solid modeling, Buildings, Urban areas, Cameras, Data models,
Unmanned aerial vehicles, Facades, image flow.
BibRef
Li, X.X.[Xiu-Xiu],
Liu, Y.J.[Yan-Juan],
Jin, H.Y.[Hai-Yan],
Zheng, J.B.[Jiang-Bin],
Cai, L.[Lei],
Automatic layered RGB-D scene flow estimation with optical flow field
constraint,
IET-IPR(14), No. 16, 19 December 2020, pp. 4092-4101.
DOI Link
2103
BibRef
Wang, G.M.[Guang-Ming],
Wu, X.R.[Xin-Rui],
Liu, Z.[Zhe],
Wang, H.S.[He-Sheng],
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds,
IP(30), 2021, pp. 5168-5181.
IEEE DOI
2106
Estimation, Task analysis, Laser radar,
Correlation, Lattices, Optical imaging, Deep learning,
LiDAR odometry
BibRef
Schuster, R.[René],
Unger, C.[Christian],
Stricker, D.[Didier],
A Deep Temporal Fusion Framework for Scene Flow Using a Learnable
Motion Model and Occlusions,
WACV21(247-255)
IEEE DOI
2106
Training, Limiting, Motion estimation,
Neural networks, Bidirectional control
BibRef
Lee, J.[Junghyup],
Kim, D.[Dohyung],
Lee, W.[Wonkyung],
Ponce, J.[Jean],
Ham, B.[Bumsub],
Learning Semantic Correspondence Exploiting an Object-Level Prior,
PAMI(44), No. 3, March 2022, pp. 1399-1414.
IEEE DOI
2202
Semantics, Training, Task analysis, Clutter, Feature extraction,
Strain, Robustness, Semantic correspondence, object-level prior,
differentiable argmax function
BibRef
Li, Y.[Yinxiao],
Lu, Z.C.[Zhi-Chao],
Xiong, X.[Xuehan],
Huang, J.[Jonathan],
PERF-Net: Pose Empowered RGB-Flow Net,
WACV22(798-807)
IEEE DOI
2202
Computational modeling, Streaming media,
Rendering (computer graphics), Kinetic theory, Standards,
Action and Behavior Recognition
BibRef
He, P.[Pan],
Emami, P.[Patrick],
Ranka, S.[Sanjay],
Rangarajan, A.[Anand],
Learning Scene Dynamics from Point Cloud Sequences,
IJCV(130), No. 3, March 2022, pp. 669-695.
Springer DOI
2203
WWW Link.
Code, Scene Flow.
BibRef
Kang, J.[Jiwoo],
Lee, S.[Seongmin],
Jang, M.Y.[Ming-Yu],
Lee, S.H.[Sang-Hoon],
Gradient Flow Evolution for 3D Fusion From a Single Depth Sensor,
CirSysVideo(32), No. 4, April 2022, pp. 2211-2225.
IEEE DOI
2204
Surface reconstruction, Pipelines, Strain, Cameras, Reliability,
Real-time systems, 3D reconstruction, signed distance field,
incremental reconstruction
BibRef
Liu, T.Y.[Tian-Yi],
Wang, Y.[Yan],
Niu, X.J.[Xiao-Ji],
Chang, L.[Le],
Zhang, T.S.[Ti-Sheng],
Liu, J.N.[Jing-Nan],
LiDAR Odometry by Deep Learning-Based Feature Points with Two-Step
Pose Estimation,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link
2206
BibRef
Yang, Y.D.[Yan-Ding],
Jiang, K.[Kun],
Yang, D.[Diange],
Jiang, Y.Q.[Yan-Qin],
Lu, X.W.[Xiao-Wei],
Temporal Point Cloud Fusion With Scene Flow for Robust 3D Object
Tracking,
SPLetters(29), 2022, pp. 1579-1583.
IEEE DOI
2208
Feature extraction, Point cloud compression, Object tracking,
Estimation, Training, Tracking, Scene flow estimation,
3D object tracking
BibRef
Liu, S.[Shu],
Barnes, N.[Nick],
Mahony, R.[Robert],
Ye, H.[Haolei],
Network-based structure flow estimation,
DICTA20(1-7)
IEEE DOI
2201
Solid modeling, Image color analysis, Motion estimation,
Estimation, Robustness, Task analysis, Optical flow
BibRef
Lu, Y.W.[Ya-Wen],
Zhu, Y.H.[Yu-Hao],
Lu, G.Y.[Guo-Yu],
3D SceneFlowNet: Self-Supervised 3D Scene Flow Estimation Based on
Graph CNN,
ICIP21(3647-3651)
IEEE DOI
2201
Measurement, Solid modeling, Image motion analysis, Laser radar,
Estimation, Predictive models, 3D Scene Flow Estimation, Graph CNN,
3D Scene Understanding
BibRef
Guo, X.Z.[Xue-Zhou],
Lin, X.[Xuhu],
Zhao, L.[Lili],
Zhu, Z.Z.[Ze-Zhi],
Chen, J.W.[Jian-Wen],
An Unsupervised Optical Flow Estimation for Lidar Image Sequences,
ICIP21(2613-2617)
IEEE DOI
2201
Image motion analysis, Laser radar, Annotations, Estimation,
Image sequences, Optical Flow, LiDAR Image Sequences, Unsupervised Learning
BibRef
Wang, H.Y.[Hai-Yan],
Pang, J.H.[Jia-Hao],
Lodhi, M.A.[Muhammad A.],
Tian, Y.L.[Ying-Li],
Tian, D.[Dong],
FESTA:
Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds,
CVPR21(14168-14177)
IEEE DOI
2111
Image recognition,
Image coding, Navigation, Computational modeling, Estimation
BibRef
Li, R.[Ruibo],
Lin, G.S.[Guo-Sheng],
Xie, L.H.[Li-Hua],
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point
Clouds with Optimal Transport and Random Walk,
CVPR21(15572-15581)
IEEE DOI
2111
Training, Costs,
Image color analysis, Supervised learning, Estimation
BibRef
Gojcic, Z.[Zan],
Litany, O.[Or],
Wieser, A.[Andreas],
Guibas, L.J.[Leonidas J.],
Birdal, T.[Tolga],
Weakly Supervised Learning of Rigid 3D Scene Flow,
CVPR21(5688-5699)
IEEE DOI
2111
Annotations, Supervised learning,
Refining, Estimation, Data collection, Pattern recognition
BibRef
Li, Z.Q.[Zheng-Qi],
Niklaus, S.[Simon],
Snavely, N.[Noah],
Wang, O.[Oliver],
Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic
Scenes,
CVPR21(6494-6504)
IEEE DOI
2111
Geometry, Solid modeling,
Dynamics, Neural networks, Cameras
BibRef
Wei, Y.[Yi],
Wang, Z.[Ziyi],
Rao, Y.M.[Yong-Ming],
Lu, J.W.[Ji-Wen],
Zhou, J.[Jie],
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of
Point Clouds,
CVPR21(6950-6959)
IEEE DOI
2111
Visualization, Correlation,
Estimation, Lattices, Transforms
BibRef
Teed, Z.[Zachary],
Deng, J.[Jia],
RAFT-3D: Scene Flow using Rigid-Motion Embeddings,
CVPR21(8371-8380)
IEEE DOI
2111
Technological innovation, Deep architecture, Optical flow
BibRef
Kittenplon, Y.[Yair],
Eldar, Y.C.[Yonina C.],
Raviv, D.[Dan],
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation,
CVPR21(4112-4121)
IEEE DOI
2111
Training, Learning systems,
Refining, Estimation, Computer architecture
BibRef
Li, R.[Ruibo],
Lin, G.S.[Guo-Sheng],
He, T.[Tong],
Liu, F.[Fayao],
Shen, C.H.[Chun-Hua],
HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order
CRFs and Position-aware Flow Embedding,
CVPR21(364-373)
IEEE DOI
2111
Deep learning, Costs, Force, Dynamics, Estimation
BibRef
Seidel, R.[Roman],
Apitzsch, A.[André],
Hirtz, G.[Gangolf],
OmniFlow: Human Omnidirectional Optical Flow,
OmniCV21(3673-3676)
IEEE DOI
2109
Training, Lighting, Estimation,
Network architecture, Rendering (computer graphics), Indoor environment
BibRef
Ouyang, B.[Bojun],
Raviv, D.[Dan],
Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point
Clouds,
3DV21(782-791)
IEEE DOI
2201
BibRef
Earlier:
Occlusion Guided Scene Flow Estimation on 3D Point Clouds,
WAD21(2799-2808)
IEEE DOI
2109
Training, Point cloud compression, Costs, Correlation,
Neural networks, Estimation.
Symbiosis, Measurement, Deep learning,
Estimation, Computer architecture, Tools
BibRef
Li, C.C.[Cong-Cong],
Ma, H.Y.[Hao-Yu],
Liao, Q.M.[Qing-Min],
Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model,
ICPR21(3876-3883)
IEEE DOI
2105
Adaptation models, Computational modeling, Neural networks,
Estimation, Feature extraction, Real-time systems, Pattern recognition
BibRef
Zuanazzi, V.[Victor],
van Vugt, J.[Joris],
Booij, O.[Olaf],
Mettes, P.S.[Pascal S.],
Adversarial Self-Supervised Scene Flow Estimation,
3DV20(1049-1058)
IEEE DOI
2102
Estimation, Measurement, Training,
Cloud computing, Benchmark testing, Loss measurement, Point Clouds,
Neural Networks
BibRef
Pontes, J.K.,
Hays, J.,
Lucey, S.,
Scene Flow from Point Clouds with or without Learning,
3DV20(261-270)
IEEE DOI
2102
Laplace equations,
Linear programming, Optical imaging, Annotations, Topology, Strain,
Laplacian
BibRef
Chen, Y.H.[Yu-Hua],
Van Gool, L.J.[Luc J.],
Schmid, C.[Cordelia],
Sminchisescu, C.[Cristian],
Consistency Guided Scene Flow Estimation,
ECCV20(VII:125-141).
Springer DOI
2011
BibRef
Li, X.T.[Xiang-Tai],
You, A.S.[An-Sheng],
Zhu, Z.[Zhen],
Zhao, H.L.[Hou-Long],
Yang, M.[Maoke],
Yang, K.Y.[Kui-Yuan],
Tan, S.H.[Shao-Hua],
Tong, Y.H.[Yun-Hai],
Semantic Flow for Fast and Accurate Scene Parsing,
ECCV20(I:775-793).
Springer DOI
2011
BibRef
Jeon, S.[Sangryul],
Min, D.B.[Dong-Bo],
Kim, S.[Seungryong],
Choe, J.[Jihwan],
Sohn, K.H.[Kwang-Hoon],
Guided Semantic Flow,
ECCV20(XXVIII:631-648).
Springer DOI
2011
BibRef
Boulch, A.[Alexandre],
Puy, G.[Gilles],
Marlet, R.[Renaud],
FKAConv: Feature-kernel Alignment for Point Cloud Convolution,
ACCV20(I:381-399).
Springer DOI
2103
BibRef
Puy, G.[Gilles],
Boulch, A.[Alexandre],
Marlet, R.[Renaud],
Flot: Scene Flow on Point Clouds Guided by Optimal Transport,
ECCV20(XXVIII:527-544).
Springer DOI
2011
BibRef
Wu, W.X.[Wen-Xuan],
Wang, Z.Y.[Zhi Yuan],
Li, Z.W.[Zhu-Wen],
Liu, W.[Wei],
Fuxin, L.[Li],
Pointpwc-net: Cost Volume on Point Clouds for (self-)supervised Scene
Flow Estimation,
ECCV20(V:88-107).
Springer DOI
2011
BibRef
Mittal, H.,
Okorn, B.,
Held, D.,
Just Go With the Flow: Self-Supervised Scene Flow Estimation,
CVPR20(11174-11182)
IEEE DOI
2008
Estimation, Training,
Autonomous vehicles, Supervised learning, Tracking, Adaptive optics
BibRef
Yang, G.,
Ramanan, D.,
Upgrading Optical Flow to 3D Scene Flow Through Optical Expansion,
CVPR20(1331-1340)
IEEE DOI
2008
Optical imaging, Cameras,
Optical sensors, Optical variables control,
Two dimensional displays
BibRef
Wang, Z.,
Li, S.,
Howard-Jenkins, H.,
Prisacariu, V.A.,
Chen, M.,
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation,
WACV20(91-98)
IEEE DOI
2006
Estimation, Feature extraction, Benchmark testing, Measurement, Training
BibRef
Rangel, J.,
Schmoll, R.,
Kroll, A.,
On Scene Flow Computation of GAS Structures with Optical GAS Imaging
Cameras,
WACV20(174-182)
IEEE DOI
2006
Cameras, Optical imaging, Estimation,
Optical variables control, Velocity measurement, Temperature measurement
BibRef
Jiang, H.,
Sun, D.,
Jampani, V.,
Lv, Z.,
Learned-Miller, E.,
Kautz, J.,
SENSE: A Shared Encoder Network for Scene-Flow Estimation,
ICCV19(3194-3203)
IEEE DOI
2004
image representation, image segmentation, image sequences,
learning (artificial intelligence), motion estimation, Decoding
BibRef
Brickwedde, F.,
Abraham, S.,
Mester, R.,
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular
Scene Flow Estimation of Dynamic Traffic Scenes,
ICCV19(2780-2790)
IEEE DOI
2004
calibration, convolutional neural nets, geometry, Task analysis,
image motion analysis, image reconstruction, image segmentation.
BibRef
Qi, X.J.[Xiao-Juan],
Liu, Z.Z.[Zheng-Zhe],
Chen, Q.F.[Qi-Feng],
Jia, J.Y.[Jia-Ya],
3D Motion Decomposition for RGBD Future Dynamic Scene Synthesis,
CVPR19(7665-7674).
IEEE DOI
2002
BibRef
Behl, A.[Aseem],
Paschalidou, D.[Despoina],
Donne, S.[Simon],
Geiger, A.[Andreas],
PointFlowNet: Learning Representations for Rigid Motion Estimation From
Point Clouds,
CVPR19(7954-7963).
IEEE DOI
2002
BibRef
Liu, X.Y.[Xing-Yu],
Qi, C.R.[Charles R.],
Guibas, L.J.[Leonidas J.],
FlowNet3D: Learning Scene Flow in 3D Point Clouds,
CVPR19(529-537).
IEEE DOI
2002
BibRef
Gu, X.[Xiuye],
Wang, Y.[Yijie],
Wu, C.R.[Chong-Ruo],
Lee, Y.J.[Yong Jae],
Wang, P.Q.[Pan-Qu],
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow
Estimation on Large-Scale Point Clouds,
CVPR19(3249-3258).
IEEE DOI
2002
BibRef
Ma, W.C.[Wei-Chiu],
Wang, S.L.[Shen-Long],
Hu, R.[Rui],
Xiong, Y.[Yuwen],
Urtasun, R.[Raquel],
Deep Rigid Instance Scene Flow,
CVPR19(3609-3617).
IEEE DOI
2002
BibRef
Richardt, C.,
Kim, H.,
Valgaerts, L.,
Theobalt, C.,
Dense Wide-Baseline Scene Flow from Two Handheld Video Cameras,
3DV16(276-285)
IEEE DOI
1701
image reconstruction
BibRef
Lv, Z.Y.[Zhao-Yang],
Beall, C.[Chris],
Alcantarilla, P.F.[Pablo F.],
Li, F.[Fuxin],
Kira, Z.[Zsolt],
Dellaert, F.[Frank],
A Continuous Optimization Approach for Efficient and Accurate Scene
Flow,
ECCV16(VIII: 757-773).
Springer DOI
1611
BibRef
Li, F.[Francis],
Wong, A.[Alexander],
Zelek, J.S.[John S.],
Hierarchical Grouping Approach for Fast Approximate RGB-D Scene Flow,
CRV16(140-147)
IEEE DOI
1612
RGB-D;scene flow;spectral clustering
BibRef
Srinivasan, P.P.[Pratul P.],
Tao, M.W.[Michael W.],
Ng, R.[Ren],
Ramamoorthi, R.[Ravi],
Oriented Light-Field Windows for Scene Flow,
ICCV15(3496-3504)
IEEE DOI
1602
Generalized optical flow.
BibRef
Sun, D.Q.[De-Qing],
Sudderth, E.B.[Erik B.],
Pfister, H.[Hanspeter],
Layered RGBD scene flow estimation,
CVPR15(548-556)
IEEE DOI
1510
BibRef
Alhaija, H.A.[Hassan Abu],
Sellent, A.[Anita],
Kondermann, D.[Daniel],
Rother, C.[Carsten],
GraphFlow: 6D Large Displacement Scene Flow via Graph Matching,
GCPR15(285-296).
Springer DOI
1511
BibRef
Zanfir, A.,
Sminchisescu, C.,
Large Displacement 3D Scene Flow with Occlusion Reasoning,
ICCV15(4417-4425)
IEEE DOI
1602
Adaptive optics
BibRef
Ferstl, D.[David],
Reinbacher, C.[Christian],
Riegler, G.[Gernot],
Ruther, M.[Matthias],
Bischof, H.[Horst],
aTGV-SF: Dense Variational Scene Flow through Projective Warping and
Higher Order Regularization,
3DV14(285-292)
IEEE DOI
1503
Cameras
BibRef
Roh, J.[Junha],
Lim, H.[Hwasup],
Ahn, S.C.[Sang Chul],
A Fast TGV-l1 RGB-D Flow Estimation,
ISVC14(I: 151-161).
Springer DOI
1501
BibRef
Hornacek, M.[Michael],
Fitzgibbon, A.W.[Andrew W.],
Rother, C.[Carsten],
SphereFlow: 6 DoF Scene Flow from RGB-D Pairs,
CVPR14(3526-3533)
IEEE DOI
1409
BibRef
Ferstl, D.[David],
Riegler, G.[Gernot],
Ruther, M.[Matthias],
Bischof, H.[Horst],
CP-Census: A Novel Model for Dense Variational Scene Flow from RGB-D
Data,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Maier, R.[Robert],
Sturm, J.[Jürgen],
Cremers, D.[Daniel],
Submap-Based Bundle Adjustment for 3D Reconstruction from RGB-D Data,
GCPR14(54-65).
Springer DOI
1411
See also Graph Based Bundle Adjustment for INS-Camera Calibration, A.
BibRef
Steinbrucker, F.[Frank],
Sturm, J.[Jurgen],
Cremers, D.[Daniel],
Real-time visual odometry from dense RGB-D images,
Dense11(719-722).
IEEE DOI
1201
BibRef
Zhang, X.W.[Xiao-Wei],
Chen, D.P.[Da-Peng],
Yuan, Z.J.[Ze-Jian],
Zheng, N.N.[Nan-Ning],
Dense Scene Flow Based on Depth and Multi-channel Bilateral Filter,
ACCV12(III:140-151).
Springer DOI
1304
BibRef
Letouzey, A.[Antoine],
Petit, B.[Benjamin],
Boyer, E.[Edmond],
Scene Flow from Depth and Color Images,
BMVC11(xx-yy).
HTML Version.
1110
BibRef
Cech, J.[Jan],
Sanchez-Riera, J.[Jordi],
Horaud, R.[Radu],
Scene flow estimation by growing correspondence seeds,
CVPR11(3129-3136).
IEEE DOI
1106
Flow in stereo
See also Topologically-robust 3D shape matching based on diffusion geometry and seed growing.
BibRef
Chapter on Optical Flow Field Computations and Use continues in
Error Analysis, Evaluation for Optical Flow .