mlsanityData to 7 Oct 2026

Sensor-Guided Optical Flow

arXiv 2109.15321 ↗First posted 30 Sep 2021cs.CVcited by 4

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Abstract (arXiv)

This paper proposes a framework to guide an optical flow network with external cues to achieve superior accuracy either on known or unseen domains. Given the availability of sparse yet accurate optical flow hints from an external source, these are injected to modulate the correlation scores computed by a state-of-the-art optical flow network and guide it towards more accurate predictions. Although no real sensor can provide sparse flow hints, we show how these can be obtained by combining depth measurements from active sensors with geometry and hand-crafted optical flow algorithms, leading to accurate enough hints for our purpose. Experimental results with a state-of-the-art flow network on standard benchmarks support the effectiveness of our framework, both in simulated and real conditions.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 52 of its 79 references that are in the map. First posted on arXiv 30 Sep 2021.

Builds on

52 of 79 references.

References · 52

  1. 2017Mask R-CNNcited by 7,259
  2. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  3. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  4. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  5. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  6. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  7. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 966
  8. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  9. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  10. 2016Optical Flow Estimation using a Spatial Pyramid Networkcited by 421
  11. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  12. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 341
38 more
  1. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  2. 2017UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Losscited by 202
  3. 2017Image to Image Translation for Domain Adaptationcited by 171
  4. 2017Modulating early visual processing by languagecited by 167
  5. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  6. 2019Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimationcited by 131
  7. 2019Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Cameracited by 130
  8. 2019SelFlow: Self-Supervised Learning of Optical Flowcited by 128
  9. 2020What Matters in Unsupervised Optical Flowcited by 124
  10. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  11. 2020MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Maskcited by 117
  12. 2019Domain-invariant Stereo Matching Networkscited by 108
  13. 2021AutoFlow: Learning a Better Training Set for Optical Flowcited by 102
  14. 2016Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothnesscited by 101
  15. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  16. 2018In Defense of Classical Image Processing: Fast Depth Completion on the CPUcited by 96
  17. 2018Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimationcited by 94
  18. 2018Hierarchical Discrete Distribution Decomposition for Match Density Estimationcited by 93
  19. 2018StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Predictioncited by 88
  20. 2020Unsupervised Domain Adaptation in Semantic Segmentation: a Reviewcited by 88
  21. 2015EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flowcited by 80
  22. 2018Real-time self-adaptive deep stereocited by 80
  23. 2019A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularizationcited by 76
  24. 2018Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimationcited by 69
  25. 2017Accurate Optical Flow via Direct Cost Volume Processingcited by 66
  26. 2019DDFlow: Learning Optical Flow with Unlabeled Data Distillationcited by 63
  27. 2021Weakly Supervised Learning of Rigid 3D Scene Flowcited by 60
  28. 2020Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimationcited by 49
  29. 2020Distilled Semantics for Comprehensive Scene Understanding from Videoscited by 39
  30. 2020LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimationcited by 37
  31. 2020Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matchingcited by 36
  32. 2016Full Flow: Optical Flow Estimation By Global Optimization over Regular Gridscited by 34
  33. 2020Matching-space Stereo Networks for Cross-domain Generalizationcited by 32
  34. 2021Learning optical flow from still imagescited by 28
  35. 2019Guided Stereo Matchingcited by 24
  36. 2020ScopeFlow: Dynamic Scene Scoping for Optical Flowcited by 24
  37. 2019Learning Across Tasks and Domainscited by 16
  38. 2019LiDAR-Flow: Dense Scene Flow Estimation from Sparse LiDAR and Stereo Imagescited by 12

50 most cited of 52 All 52 (JSON)

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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2023RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimationcited by 26
  2. 2022Unsupervised confidence for LiDAR depth maps and applicationscited by 10
  3. 2023Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusioncited by 10
  4. 2022Multi-View Guided Multi-View Stereocited by 5