mlsanityData to 7 Oct 2026

A Recursive Pyramidal Algorithm for Solving the Image Registration Problem

arXiv 2510.04231 ↗First posted 5 Oct 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The problem of image registration is finding a transformation that aligns two images, such that the corresponding points are in the same location. This paper introduces a simple, end-to-end trainable algorithm that is implementable in a few lines of Python code. The approach is shown to work with very little training data and training time, while achieving accurate results in some settings. An example application to stereo vision was trained from 74 images on a 19x15 input window. With just a dozen lines of Python code this algorithm excels in brevity and may serve as a good start in related scenarios with limitations to training data, training time or code complexity.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 5 Oct 2025.

Builds on

13 of 28 references.

References · 13

  1. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  2. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  3. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  4. 2018Pyramid Stereo Matching Networkcited by 486
  5. 2013OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networkscited by 224
  6. 2020Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformerscited by 190
  7. 2015Stereo Matching by Training a Convolutional Neural Network to Compare Image Patchescited by 151
  8. 2015Learning to Compare Image Patches via Convolutional Neural Networkscited by 109
  9. 2014Computing the Stereo Matching Cost with a Convolutional Neural Networkcited by 74
  10. 2017Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matchingcited by 63
  11. 2021The Functional Correspondence Problemcited by 20
  12. 2024UniTT-Stereo: Unified Training of Transformer for Enhanced Stereo Matchingcited by 3
1 more
  1. 2022Stereo Matching with Cost Volume based Sparse Disparity Propagationcited by 1

Built on by

Not cited yet.