# Fast Optical Flow using Dense Inverse Search

arXiv: https://arxiv.org/abs/1603.03590 · HTML: https://mlsanity.com/paper/1603.03590/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2016.

## Built on by (most cited first, 30 of 71)

- [Many-to-many Splatting for Efficient Video Frame Interpolation](https://mlsanity.com/paper/2204.03513/) (2022)
- [DIP: Deep Inverse Patchmatch for High-Resolution Optical Flow](https://mlsanity.com/paper/2204.00330/) (2022)
- [FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation](https://mlsanity.com/paper/2207.08119/) (2022)
- [HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture](https://mlsanity.com/paper/2112.06904/) (2021)
- [FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation](https://mlsanity.com/paper/2103.04524/) (2021)
- [Local Memory Attention for Fast Video Semantic Segmentation](https://mlsanity.com/paper/2101.01715/) (2021)
- [GaussianTalker: Speaker-specific Talking Head Synthesis via 3D Gaussian Splatting](https://mlsanity.com/paper/2404.14037/) (2024)
- [GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning](https://mlsanity.com/paper/2103.13725/) (2021)
- [High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation](https://mlsanity.com/paper/2103.15876/) (2021)
- [AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies](https://mlsanity.com/paper/2211.05709/) (2022)
- [GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning](https://mlsanity.com/paper/2301.10018/) (2023)
- [Depth Perspective-aware Multiple Object Tracking](https://mlsanity.com/paper/2207.04551/) (2022)
- [MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation](https://mlsanity.com/paper/2111.14646/) (2021)
- [360deg Optical Flow using Tangent Images](https://mlsanity.com/paper/2112.14331/) (2021)
- [Lagrangian Motion Magnification with Double Sparse Optical Flow Decomposition](https://mlsanity.com/paper/2204.07636/) (2022)
- [Real-time event simulation with frame-based cameras](https://mlsanity.com/paper/2209.04634/) (2022)
- [ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation](https://mlsanity.com/paper/2102.13493/) (2021)
- [Deep Learning for Robust Motion Segmentation with Non-Static Cameras](https://mlsanity.com/paper/2102.10929/) (2021)
- [Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor](https://mlsanity.com/paper/2204.01154/) (2022)
- [SimpleGVR: A Simple Baseline for Latent-Cascaded Video Super-Resolution](https://mlsanity.com/paper/2506.19838/) (2025)
- [Spherical formulation of geometric motion segmentation constraints in fisheye cameras](https://mlsanity.com/paper/2104.12404/) (2021)
- [BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo Surgical Image Matching](https://mlsanity.com/paper/2205.03133/) (2022)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation](https://mlsanity.com/paper/2409.17001/) (2024)
- [Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising](https://mlsanity.com/paper/2412.11820/) (2024)
- [PriOr-Flow: Enhancing Primitive Panoramic Optical Flow with Orthogonal View](https://mlsanity.com/paper/2506.23897/) (2025)
- [Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders](https://mlsanity.com/paper/2101.03603/) (2021)
- [Bayesian dense inverse searching algorithm for real-time stereo matching in minimally invasive surgery](https://mlsanity.com/paper/2106.07136/) (2021)
- [Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency](https://mlsanity.com/paper/2106.14336/) (2021)
- [DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation](https://mlsanity.com/paper/2203.14481/) (2022)

## Abstract (arXiv)

Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity. However, in real-life visual applications, such as tracking, activity detection and recognition, the time complexity is critical. We propose a solution with very low time complexity and competitive accuracy for the computation of dense optical flow. It consists of three parts: 1) inverse search for patch correspondences; 2) dense displacement field creation through patch aggregation along multiple scales; 3) variational refinement. At the core of our Dense Inverse Search-based method (DIS) is the efficient search of correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews in 2001. DIS is competitive on standard optical flow benchmarks with large displacements. DIS runs at 300Hz up to 600Hz on a single CPU core, reaching the temporal resolution of human's biological vision system. It is order(s) of magnitude faster than state-of-the-art methods in the same range of accuracy, making DIS ideal for visual applications.