17.3.2 Scene Flow, Depth Image Flow, RGB-D

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], Moses, Y.[Yael], Kiryati, N.[Nahum],
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.W.[Bing-Wei], 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.W.[Bing-Wei], Zhang, L.W.[Li-Wei], 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, J.[Jian], Song, N.[Na], Xia, Z.[Zhengde], Liu, B.[Bin], Pan, J.X.[Jin-Xiao], Ghaffar, A.[Abdul], Ren, J.B.[Jian-Bin], Yang, M.[Ming],
A dense light field reconstruction algorithm for four-dimensional optical flow constraint equation,
PR(134), 2023, pp. 109101.
Elsevier DOI 2212
Light field, Optical flow, A dense reconstruction BibRef

Fan, H.[Hehe], Yu, X.[Xin], Yang, Y.[Yi], Kankanhalli, M.[Mohan],
Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition,
PAMI(44), No. 12, December 2022, pp. 9918-9930.
IEEE DOI 2212
Videos, Point cloud compression, Electron tubes, Convolution, Semantics, Feature extraction, Point cloud, scene flow estimation BibRef

Fan, H.[Hehe], Yang, Y.[Yi], Kankanhalli, M.[Mohan],
Point Spatio-Temporal Transformer Networks for Point Cloud Video Modeling,
PAMI(45), No. 2, February 2023, pp. 2181-2192.
IEEE DOI 2301
Point cloud compression, Transformers, Encoding, Computational modeling, Adaptation models, Solid modeling, video analysis BibRef

Cheng, W.C.[Wen-Can], Ko, J.H.[Jong Hwan],
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation,
ECCV22(XXVIII:108-124).
Springer DOI 2211
BibRef

Bendig, K.[Katharina], Schuster, R.[René], Stricker, D.[Didier],
Self-Superflow: Self-Supervised Scene Flow Prediction in Stereo Sequences,
ICIP22(481-485)
IEEE DOI 2211
Training, Deep learning, Annotations, Neural networks, Transforms, Benchmark testing, Scene flow, Self-supervision, Occlusion, Stereo BibRef

Ding, L.[Lihe], Dong, S.[Shaocong], Xu, T.[Tingfa], Xu, X.[Xinli], Wang, J.[Jie], Li, J.[Jianan],
FH-Net: A Fast Hierarchical Network for Scene Flow Estimation on Real-World Point Clouds,
ECCV22(XXIX:213-229).
Springer DOI 2211
BibRef

Wang, G.M.[Guang-Ming], Hu, Y.Z.[Yun-Zhe], Liu, Z.[Zhe], Zhou, Y.Y.[Yi-Yang], Tomizuka, M.[Masayoshi], Zhan, W.[Wei], Wang, H.S.[He-Sheng],
What Matters for 3D Scene Flow Network,
ECCV22(XXXIII:38-55).
Springer DOI 2211
BibRef

Erçelik, E.[Emeç], Yurtsever, E.[Ekim], Liu, M.Y.[Ming-Yu], Yang, Z.J.[Zhi-Jie], Zhang, H.Z.[Han-Zhen], Topçam, P.[Pinar], Listl, M.[Maximilian], Çayli, Y.K.[Yilmaz Kaan], Knoll, A.[Alois],
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone,
ECCV22(X:247-265).
Springer DOI 2211
BibRef

Li, R.[Runfa], Nguyen, T.[Truong],
MonoPLFlowNet: Permutohedral Lattice FlowNet for Real-Scale 3D Scene Flow Estimation with Monocular Images,
ECCV22(XXVII:322-339).
Springer DOI 2211
BibRef

Jin, Z.[Zhao], Lei, Y.[Yinjie], Akhtar, N.[Naveed], Li, H.F.[Hai-Feng], Hayat, M.[Munawar],
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds,
CVPR22(7223-7233)
IEEE DOI 2210
Point cloud compression, Training, Shape, Navigation, Estimation, Distortion, Data models, Transfer/low-shot/long-tail learning, Scene analysis and understanding BibRef

Dong, G.[Guanting], Zhang, Y.[Yueyi], Li, H.L.[Han-Lin], Sun, X.Y.[Xiao-Yan], Xiong, Z.W.[Zhi-Wei],
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation,
CVPR22(12766-12775)
IEEE DOI 2210
Measurement errors, Laser radar, Recurrent neural networks, Estimation, Optimization methods, Distortion, Reflection, Motion and tracking BibRef

Li, R.B.[Rui-Bo], Zhang, C.[Chi], Lin, G.S.[Guo-Sheng], Wang, Z.[Zhe], Shen, C.H.[Chun-Hua],
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior,
CVPR22(16938-16947)
IEEE DOI 2210
Point cloud compression, Training, Airplanes, Motion estimation, Self-supervised learning, Rigidity, Self- semi- meta- unsupervised learning BibRef

Baur, S.A.[Stefan Andreas], Emmerichs, D.J.[David Josef], Moosmann, F.[Frank], Pinggera, P.[Peter], Ommer, B.[Björn], Geiger, A.[Andreas],
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation,
ICCV21(13106-13116)
IEEE DOI 2203
Training, Point cloud compression, Laser radar, Motion segmentation, Performance gain, Motion and tracking, Vision for robotics and autonomous vehicles 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.G., 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.J.[Yi-Jie], 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 .