JOINT 3D ESTIMATION OF VEHICLES AND SCENE FLOW

111 citations as recorded by crossref.

1. Multi-scale local–global deep optical flow network for river surface velocity measurement from drone video E. Zhang et al. https://doi.org/10.1063/5.0322760
2. SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching H. Du et al. https://doi.org/10.1109/LRA.2021.3101523
3. Real-Time 3D Scene Understanding for Road Safety: Depth Estimation and Object Detection for Autonomous Vehicle Awareness M. Simeonov et al. https://doi.org/10.3390/vehicles8020028
4. Equal Baseline Camera Array—Calibration, Testbed and Applications A. Kaczmarek & B. Blaschitz https://doi.org/10.3390/app11188464
5. Motion Decoupling Network for Intra-Operative Motion Estimation Under Occlusion G. Bian et al. https://doi.org/10.1109/TMI.2023.3268774
6. 3D Point-Voxel Correlation Fields for Scene Flow Estimation Z. Wang et al. https://doi.org/10.1109/TPAMI.2023.3294355
7. Stereo matching on images based on volume fusion and disparity space attention L. Liao et al. https://doi.org/10.1016/j.engappai.2024.108902
8. Real-time stereo matching with bilateral interaction aggregation for autonomous driving X. Zhao et al. https://doi.org/10.1016/j.engappai.2026.115465
9. FPGA-based stereo matching for crop height measurement using monocular camera I. Firmansyah et al. https://doi.org/10.1016/j.micpro.2024.105063
10. OmniGlasses: an optical aid for stereo vision CNNs to enable omnidirectional image processing J. Seuffert et al. https://doi.org/10.1007/s00138-024-01534-2
11. STFlow: Self-Taught Optical Flow Estimation Using Pseudo Labels Z. Ren et al. https://doi.org/10.1109/TIP.2020.3024015
12. RWZC: A Model-Driven Approach for Learning-Based Robust Wyner-Ziv Coding Y. Shi et al. https://doi.org/10.1109/JSAC.2025.3559128
13. Revisiting Dead Leaves Model: Training With Synthetic Data P. Madhusudana et al. https://doi.org/10.1109/LSP.2021.3132289
14. PWTF: perceive wide and track fine for optical flow Z. Zou et al. https://doi.org/10.1088/1361-6501/ae9f7b
15. Information from imagery: ISPRS scientific vision and research agenda J. Chen et al. https://doi.org/10.1016/j.isprsjprs.2015.09.008
16. SemFlow: Semantic-Driven Interpolation for Large Displacement Optical Flow X. Wang et al. https://doi.org/10.1109/ACCESS.2019.2911021
17. Efficient Stereo Depth Estimation for Pseudo-LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder S. Hossain & X. Lin https://doi.org/10.3390/s23031650
18. SDF-MAN: Semi-Supervised Disparity Fusion with Multi-Scale Adversarial Networks C. Pu et al. https://doi.org/10.3390/rs11050487
19. GFANet: Group Fusion Aggregation Network for Real Time Stereo Matching Y. Zhang & J. Zhang https://doi.org/10.1109/LRA.2023.3280818
20. Learning Efficient Stereo Matching Network With Depth Discontinuity Aware Super-Resolution C. Guo et al. https://doi.org/10.1109/ACCESS.2019.2950924
21. Calculating Depth for Dark Images Using Self-Supervised Learning: An Investigation of the Role of Edge Maps as Input S. Dubey et al. https://doi.org/10.1109/ACCESS.2025.3556610
22. Scene Flow Estimation for Autonomous Driving via Correlation Compensation and Initial Motion Check M. Zhai et al. https://doi.org/10.1109/TITS.2025.3574182
23. 3D Vision System for a Robotic Arm Based on Equal Baseline Camera Array A. Kaczmarek https://doi.org/10.1007/s10846-019-01117-8
24. Street-side vehicle detection, classification and change detection using mobile laser scanning data W. Xiao et al. https://doi.org/10.1016/j.isprsjprs.2016.02.007
25. Depth estimation for advancing intelligent transport systems based on self‐improving pyramid stereo network Y. Tian et al. https://doi.org/10.1049/iet-its.2019.0462
26. Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology S. Jiang et al. https://doi.org/10.34133/plantphenomics.0245
27. Enhancing Distributed Source Coding With Encoder-Centric Frequency Adaptation and Spatial Transformation H. Xu et al. https://doi.org/10.1109/TMM.2024.3521700
28. ADfM-Net: An Adversarial Depth-From-Motion Network Based on Cross Attention and Motion Enhanced Y. Long et al. https://doi.org/10.1109/LRA.2023.3268885
29. Domain adaptive stereo matching with consistency-aware learning and monocular depth cues S. Zhang et al. https://doi.org/10.1007/s13042-026-03295-y
30. 双目立体视觉研究进展与应用 杨. Yang Xiaoli et al. https://doi.org/10.3788/LOP230457
31. Learning graph-based representations for scene flow estimation M. Zhai et al. https://doi.org/10.1007/s11042-023-15541-4
32. GPDF-Net: geometric prior-guided stereo matching with disparity fusion refinement Q. Zhao et al. https://doi.org/10.1007/s00371-024-03459-3
33. Deep scene flow learning from point cloud with Transformer X. Xiang et al. https://doi.org/10.1016/j.neucom.2025.130350
34. Active Scene Flow Estimation for Autonomous Driving via Real-Time Scene Prediction and Optimal Decision S. Wang et al. https://doi.org/10.1109/TITS.2023.3332399
35. Convergent binocular stereo: Depth perception for humanoid robot vision M. Chi & J. Tsotsos https://doi.org/10.1126/scirobotics.aec7205
36. APCAFlow: All-Pairs Cost Volume Aggregation for Optical Flow Estimation M. Feng et al. https://doi.org/10.1109/TMM.2024.3385669
37. A binocular reconstruction based on perspective projection constraints and its application on robot eye‐hand coordination H. Wei & L. Meng https://doi.org/10.1049/cvi2.12091
38. HDM-Stereo: A Plug-and-Play Framework for Stereo Matching via Hierarchical Dynamic Matching Q. Ouyang et al. https://doi.org/10.1109/JSTARS.2025.3646196
39. A robust framework for multi-view stereopsis W. Mao et al. https://doi.org/10.1007/s00371-021-02087-5
40. Computer Vision for Autonomous Vehicles J. Janai et al. https://doi.org/10.1561/0600000079
41. Scene flow estimation from 3D point clouds based on dual‐branch implicit neural representations M. Zhai et al. https://doi.org/10.1049/cvi2.12237
42. Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and Their Enabling Semantic Applications N. Islam & S. Shin https://doi.org/10.1109/OJCOMS.2024.3425314
43. Extended Reality With HMD-Assisted Guidance and Console 3D Overlay for Robotic Surgery Remote Mentoring C. Li et al. https://doi.org/10.1109/LRA.2024.3455936
44. StereoPairFree: Self-Constructed Stereo Correspondence Network From Natural Images V. Dinh & T. Choi https://doi.org/10.1109/MIS.2022.3193697
45. Detection of Cyclist’s Crossing Intention Based on Posture Estimation for Autonomous Driving A. Abadi et al. https://doi.org/10.1109/JSEN.2023.3234153
46. Unsupervised learning of optical flow in a multi-frame dynamic environment using temporal dynamic modeling Z. Sun et al. https://doi.org/10.1007/s40747-023-01266-2
47. Robust and Flexible Omnidirectional Depth Estimation With Multiple 360‐Degree Cameras M. Li et al. https://doi.org/10.1049/ipr2.70217
48. Object Scene Flow M. Menze et al. https://doi.org/10.1016/j.isprsjprs.2017.09.013
49. Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis A. Kashevnik et al. https://doi.org/10.3390/s21113771
50. Global Context Volume Construction and Semantics-guided Disparity Refinement for Stereo Matching F. Xu et al. https://doi.org/10.1016/j.neucom.2024.128995
51. Motion Vector Extrapolation for Video Object Detection J. True & N. Khan https://doi.org/10.3390/jimaging9070132
52. ARUNet: Advancing Real-Time Stereo Matching for Robotic Perception on Edge Devices B. Liang et al. https://doi.org/10.1109/LRA.2025.3589145
53. Stereo Superpixel Segmentation via Decoupled Dynamic Spatial-Embedding Fusion Network H. Li et al. https://doi.org/10.1109/TMM.2023.3265843
54. Multiscale Neighborhood Cluster Scene Flow Prior for LiDAR Point Clouds J. Gan et al. https://doi.org/10.1109/TGRS.2024.3520209
55. Self-supervised recurrent depth estimation with attention mechanisms I. Makarov et al. https://doi.org/10.7717/peerj-cs.865
56. EBStereo: edge-based loss function for real-time stereo matching W. Bi et al. https://doi.org/10.1007/s00371-023-03002-w
57. Estimation and Propagation: Scene Flow Prediction on Occluded Point Clouds K. Wang & S. Shen https://doi.org/10.1109/LRA.2022.3215019
58. Vehicle Localization Using 3D Building Models and Point Cloud Matching A. Ballardini et al. https://doi.org/10.3390/s21165356
59. Deep Shape and SVBRDF Estimation using Smartphone Multi‐lens Imaging C. Fan et al. https://doi.org/10.1111/cgf.14972
60. 基于改进ACV模型的视差估计方法 秦. Qin Lunming et al. https://doi.org/10.3788/LOP240835
61. Mgs-Stereo: Multi-Scale Geometric-Structure-Enhanced Stereo Matching for Complex Real-World Scenes Z. Dai et al. https://doi.org/10.1109/TIP.2025.3612754
62. Parameter selection framework for stereo correspondence P. Nguyen & C. Ahn https://doi.org/10.1007/s00138-020-01076-3
63. Automatic Pseudo-LiDAR Annotation: Generation of Training Data for 3D Object Detection Networks C. Oh et al. https://doi.org/10.1109/ACCESS.2024.3355137
64. OrangeStereo: A navel orange stereo matching network for 3D surface reconstruction Y. Gao et al. https://doi.org/10.1016/j.compag.2024.108626
65. Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends Z. Li et al. https://doi.org/10.1111/cgf.14795
66. Hierarchical Attention Learning of Scene Flow in 3D Point Clouds G. Wang et al. https://doi.org/10.1109/TIP.2021.3079796
67. Improving IMU-Based Prediction of Lower Limb Kinematics in Natural Environments Using Egocentric Optical Flow A. Sharma & E. Rombokas https://doi.org/10.1109/TNSRE.2022.3156884
68. SVRS: self-supervised 3D voxel reconstruction network from stereo vision Z. Zou et al. https://doi.org/10.1038/s41598-026-45924-0
69. A Stereo Matching Algorithm for High‐Precision Guidance in a Weakly Textured Industrial Robot Environment Dominated by Planar Facets H. Wei & L. Meng https://doi.org/10.1111/cgf.14435
70. Content Adaptive Distributed Joint Source-Channel Coding for Image Transmission With Hyperprior Y. Li et al. https://doi.org/10.1109/TCCN.2024.3438371
71. Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching J. Wang et al. https://doi.org/10.3390/app14188457
72. Efficient Stereo Matching With Adaptive Disparity Range Scaling and Feature Calibration S. Huang et al. https://doi.org/10.1109/JSEN.2024.3491173
73. Coatrsnet: Fully Exploiting Convolution and Attention for Stereo Matching by Region Separation J. Cheng et al. https://doi.org/10.1007/s11263-023-01872-0
74. Road Damage Detection Based on Unsupervised Disparity Map Segmentation R. Fan & M. Liu https://doi.org/10.1109/TITS.2019.2947206
75. Point cloud-based scene flow estimation on realistically deformable objects: A benchmark of deep learning-based methods N. Hermes et al. https://doi.org/10.1016/j.jvcir.2023.103893
76. Pothole Detection Based on Disparity Transformation and Road Surface Modeling R. Fan et al. https://doi.org/10.1109/TIP.2019.2933750
77. MRF-Based Disparity Upsampling Using Stereo Confidence Evaluations X. Meng et al. https://doi.org/10.1109/LSP.2017.2787727
78. Building a Stereo and Wide-View Hybrid RGB/FIR Imaging System for Autonomous Vehicle Y. Zhang et al. https://doi.org/10.1109/JSEN.2021.3131342
79. Multi-Dimensional Cooperative Network for Stereo Matching W. Chen et al. https://doi.org/10.1109/LRA.2021.3130984
80. Motion estimation for large displacements and deformations Q. Chen & C. Poullis https://doi.org/10.1038/s41598-022-21987-7
81. Consistent video depth estimation X. Luo et al. https://doi.org/10.1145/3386569.3392377
82. Efficient Online Segmentation for Sparse 3D Laser Scans I. Bogoslavskyi & C. Stachniss https://doi.org/10.1007/s41064-016-0003-y
83. CEDFlow++: Latent Contour Enhancement for Dark Optical Flow Estimation F. Zuo et al. https://doi.org/10.1007/s11263-025-02528-x
84. Neural Distributed Source Coding J. Whang et al. https://doi.org/10.1109/JSAIT.2024.3412976
85. Unsupervised Learning of 3D Scene Flow With LiDAR Odometry Assistance G. Wang et al. https://doi.org/10.1109/TITS.2025.3538765
86. Learning Optical Flow, Depth, and Scene Flow Without Real-World Labels V. Guizilini et al. https://doi.org/10.1109/LRA.2022.3145057
87. S2Net: A Multitask Learning Network for Semantic Stereo of Satellite Image Pairs P. Liao et al. https://doi.org/10.1109/TGRS.2023.3335997
88. Multi-View Depth Estimation by Using Adaptive Point Graph to Fuse Single-View Depth Probabilities K. Wang et al. https://doi.org/10.1109/LRA.2024.3405332
89. Efficient and accurate stereo matching via guided deformable aggregation J. Li et al. https://doi.org/10.1117/1.JEI.34.2.023060
90. Self-Supervised 3D Scene Flow Estimation and Motion Prediction Using Local Rigidity Prior R. Li et al. https://doi.org/10.1109/TPAMI.2024.3401029
91. Learning Dense and Continuous Optical Flow From an Event Camera Z. Wan et al. https://doi.org/10.1109/TIP.2022.3220938
92. Simultaneous Stereo Matching and Confidence Estimation Network T. Schmähling et al. https://doi.org/10.3390/jimaging10080198
93. Stereo disparity estimation based on Siamese network Z. Zhou et al. https://doi.org/10.1088/1361-6501/ad8253
94. Adaptive Parallax-Based Contrast Enhancement for Power-Efficient Stereoscopic OLED Displays F. Huang & S. Ruan https://doi.org/10.1109/TIP.2026.3728614
95. Adaptively identify and refine ill-posed regions for accurate stereo matching C. Liu et al. https://doi.org/10.1016/j.neunet.2024.106394
96. Domain gap in adapting self-supervised depth estimation methods for stereo-endoscopy L. Sharan et al. https://doi.org/10.1515/cdbme-2020-0004
97. 3DSF-MixNet: Mixer-Based Symmetric Scene Flow Estimation From 3D Point Clouds S. Wang et al. https://doi.org/10.1109/LRA.2023.3335776
98. CR-FusionNet: Hybrid Stereo-Monocular Fusion for Depth Perception in Autonomous and Robotic Systems H. Ryu et al. https://doi.org/10.1109/ACCESS.2026.3666848
99. Recurrent convolutional model based on gated spiking neural P system for stereo matching networks C. Guo et al. https://doi.org/10.1007/s10489-023-05091-5
100. Optical Flow as Spatial-Temporal Attention Learners Y. Lu et al. https://doi.org/10.1109/TPAMI.2024.3463648
101. Split-Attention Multiframe Alignment Network for Image Restoration Y. Yu et al. https://doi.org/10.1109/ACCESS.2020.2967028
102. Group‐Based Atrous Convolution Stereo Matching Network Q. Zou et al. https://doi.org/10.1155/2021/7386280
103. Unsupervised Learning for Stereo Matching Using Single-View Videos P. Hong & C. Ahn https://doi.org/10.1109/ACCESS.2020.2987875
104. Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC O. Rahnama et al. https://doi.org/10.1109/TCSII.2019.2909169
105. Adaptable 2D to 3D Stereo Vision Image Conversion Based on a Deep Convolutional Neural Network and Fast Inpaint Algorithm T. Hachaj https://doi.org/10.3390/e25081212
106. Twin Fused Semantic Communication for Image Transmission With Equalization Y. Lei & Y. Zhang https://doi.org/10.1109/LWC.2026.3667169
107. Rethinking Road Surface 3-D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation R. Fan et al. https://doi.org/10.1109/TCYB.2021.3060461
108. DiffuVolume: Diffusion Model for Volume based Stereo Matching D. Zheng et al. https://doi.org/10.1007/s11263-025-02362-1
109. Supervised biadjacency networks for stereo matching H. Sun et al. https://doi.org/10.1007/s11042-023-15362-5
110. Improved Discrete Optical Flow Estimation With Triple Image Matching Cost F. Yang et al. https://doi.org/10.1109/ACCESS.2020.2968180
111. Deep Learning-Based Incorporation of Planar Constraints for Robust Stereo Depth Estimation in Autonomous Vehicle Applications W. Chuah et al. https://doi.org/10.1109/TITS.2021.3060001