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Awesome Optical Flow Paper list

A list of the most cited papers of optical flow survey, optical flow estimation (from classical methods to deep neural network methods), optical flow dataset & evaluation, its spatiotemporal applications especially on video segmentation with supervoxel.

The total number of papers in the list is 72.

Survey: Recent Advances in Optical Flow: Computation and Spatiotemporal Applications

Xiaohui Huang

Optical Flow Survey / Background / Dataset & Evaluation

  • Determining Optical Flow (Artificial intelligence 1981), Berthold K.P. Horn et al. [pdf]
  • Optical flow estimation: Advances and comparisons (ECCV 1994), M. Otte et al. [pdf]
  • Optical flow modeling and computation: A survey (CVIU), Denis Fortun et al. [pdf]
  • The computation of optical flow (ACM Computing Surveys), S. S. Beauchemin et al. [pdf]
  • On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results (Artificial intelligence 1981), Hans-Hellmut Nagel. [pdf]
  • Processing differential image motion (OSA 1985), J. H. Rieger. [pdf]
  • Performance of Optical Flow Techniques (IJCV), JL Barron et al. [pdf]
  • A Database and Evaluation Methodology for Optical Flow (IJCV), Simon Baker et al. [pdf]
  • A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation (CVPR 2016), Nikolaus Mayer. [pdf]
  • Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite (CVPR 2012), Andreas Geiger et al. [pdf]
  • A Naturalistic Open Source Movie for Optical Flow Evaluation (ECCV 2012), Daniel J Butler at al. [pdf]
  • Lessons and Insights from Creating a Synthetic Optical Flow Benchmark (ECCV 2012), Jonas Wulf et al. [pdf]
  • Segmenting Video Into Classes of Algorithm-Suitability (CVPR 2010), Oisin Mac Aodha et al. [pdf]
  • On Benchmarking Optical Flow (CVIU), B. McCane. [pdf]

Optical Flow Estimation with Classic Techniques

  • Optical Flow Estimation, David J. Fleet et al. [pdf]
  • Secrets of optical flow estimation and their principles (CVPR 2010), Deqing Sun et al. [pdf]
  • Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization (PAMI), Joseph K. Kearney et al. [pdf]
  • High Accuracy Optical Flow Estimation Based on a Theory for Warping (ECCV 2004), Thomas Brox et al. [pdf]
  • The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields (CVIU), Michael J.Black et al. [pdf]
  • Generalized image matching by the method of differences (1985), Bruce David Lucas. [pdf]
  • Spatiotemporal energy models for the perception of motion (1985), Edward H. Adelson et al. [pdf]
  • Elaborated Reichardt detectors (1985), Jan P. H. van Santen et al. [pdf]
  • Model of human visual-motion sensing , Andrew B. Watson et al. [pdf]
  • A Computational Framework and an Algorithm for the Measurement of Visual Motion (IJCV), P. Anandan. [pdf]
  • The Laplacian Pyramid as a Compact Image Code (1983), Peter J. Burt et al. [pdf]
  • Parallel Optical Flow Using Local Voting , James J. Little et al. [pdf]
  • Analysis of Differential and Matching Methods for Optical Flow , James J. Little et al. [pdf]
  • Measuring visual motion from image sequences (1987), Padmanabhan Anandan. [pdf]
  • Computation of component image velocity from local phase information (IJCV), David J. Fleet et al. [pdf]
  • Real-time early vision neurocomputing (2015), David A. Fay. [pdf]

Optical Flow Estimation with Deep Learning

  • A fast learning algorithm for deep belief nets (Neural Computation 2006), Geoffrey E. Hinton et al. [pdf]
  • Deep learning (Nature 2015), Yann LeCun et al. [pdf]
  • Object Recognition from Local Scale-Invariant Features (ICCV 1999), David G.Loew. [pdf]
  • Histograms of Oriented Gradients for Human Detection (CVPR 2005), Navneet Dalal et al. [pdf]
  • An Identity-Authentication System Using Fingerprints (1997), Anil K Jain et al. [pdf]
  • Rapid Object Detection using a Boosted Cascade of Simple Features (CVPR 2001), Paul Viola et al. [pdf]
  • Object Detection with Discriminatively Trained Part Based Models (PAMI), Pedro F. Felzenszwalb et al. [pdf]
  • Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks (CACM 2011), Honglak Lee et al. [pdf]
  • Gradient-Based Learning Applied to Document Recognition (1998), Yann LeCun et al. [pdf]
  • ImageNet Classification with Deep Convolutional Neural Networks (NIPS), Alex Krizhevsky et al. [pdf]
  • Network In Network (ICLR 2014), Min Lin et al. [pdf]
  • Going deeper with convolutions (CVPR 2015), Christian Szegedy et al. [pdf]
  • Very Deep Convolutional Networks for Large-Scale Image Recognition (2014), Karen Simonyan et al. [pdf]
  • Rethinking the Inception Architecture for Computer Vision (CVPR 2016), Christian Szegedy et al. [pdf]
  • Deep Residual Learning for Image Recognition (CVPR 2016), Kaiming He et al. [pdf]
  • Densely Connected Convolutional Networks (CVPR 2017), Gao Huang et al. [pdf]
  • FlowNet: Learning Optical Flow with Convolutional Networks (CVPR 2015), Alexey Dosovitskiy et al. [pdf]
  • FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks (CVPR 2017), Eddy Ilg et al. [pdf]
  • DeepFlow: Large displacement optical flow with deep matching (ICCV 2013), Philippe Weinzaepfel et al. [pdf]
  • EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow (CVPR 2015), Jerome Revaud et al. [pdf]
  • Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation (CVPR 2015), Christian Bailer et al. [pdf]
  • Guided Optical Flow Learning (CVPR 2017), Yi Zhu et al. [pdf]
  • Optical Flow Estimation using a Spatial Pyramid Network (CVPR 2017), Anurag Ranjan et al. [pdf]
  • Unsupervised Deep Learning for Optical Flow Estimation (AAAI 2017), Zhe Ren et al. [pdf]
  • CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss (CVPR 2017), Christian Bailer et al. [pdf]
  • Discriminative Learning of Deep Convolutional Feature Point Descriptors (ICCV 2015), Edgar Simo-Serra et al. [pdf]
  • Dense Optical Flow Prediction from a Static Image (ICCV 2015), Jacob Walker et al. [pdf]

Spatiotemporal Applications / Supervoxel / Video Segmentation

  • Evaluation of Super-Voxel Methods for Early Video Processing (CVPR 2012), Chenliang Xu and Jason J. Corso. [pdf]
  • Flattening Supervoxel Hierarchies by the Uniform Entropy Slice (ICCV 2013), Chenliang Xu et al. [pdf]
  • Supervoxel-based Segmentation of 3D Volumetric Images (ACCV 2016), Chengliang Yang et al. [pdf]
  • Streaming Hierarchical Video Segmentation (ECCV 2012), Chenliang Xu et al. [pdf]
  • Coarse-to-fine Semantic Video Segmentation using Supervoxel Trees (ICCV 2013), Aastha Jain et al. [pdf]
  • An Efficient Online Hierarchical Supervoxel Segmentation Algorithm for Time-critical Applications (BMVC 2014), Yiliang Xu et al. [pdf]
  • Unsupervised Learning of Supervoxel Embeddings for Video Segmentation (ICPR 2016), Mehran Khodabandeh et al. [pdf]
  • A supervoxel-based segmentation method for prostate MR images (2015), Zhiqiang Tian et al. [pdf]
  • Video Object Segmentation using Supervoxel-Based Gerrymandering (2017), Brent A. Griffin et al. [pdf]
  • One-Shot Video Object Segmentation (CVPR 2017), S. Caelles, K.K. Maninis et al. [pdf]
  • Video Segmentation via Object Flow (CVPR 2016), Y.-H. Tsai et al. [pdf]
  • Learning Video Object Segmentation from Static Images (CVPR 2017), F. Perazzi, A. Khoreva et al. [pdf]
  • Online Adaptation of Convolutional Neural Networks for Video Object Segmentation (BMVC 2017), P. Voigtlaender et al. [pdf]
  • Unsupervised Learning of Supervoxel Embeddings for Video Segmentation (ICPR 2016), Mehran Khodabandeh et al. [pdf]
  • SegFlow: Joint Learning for Video Object Segmentation and Optical Flow (ICCV 2017), J.Cheng et al. [pdf]