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A review on vision-based motion estimation

arXiv 2407.14478 ↗First posted 19 Jul 2024cs.CVcited by 0

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Abstract (arXiv)

Compared to contact sensors-based motion measurement, vision-based motion measurement has advantages of low cost and high efficiency and have been under active development in the past decades. This paper provides a review on existing motion measurement methods. In addition to the development of each branch of vision-based motion measurement methods, this paper also discussed the advantages and disadvantages of existing methods. Based on this discussion, it was identified that existing methods have a common limitation in optimally balancing accuracy and robustness. To address issue, we developed the Gaussian kernel-based motion measurement method. Preliminary study shows that the developed method can achieve high accuracy on simple synthesized images.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 79 references that are in the map. First posted on arXiv 19 Jul 2024.

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References · 8

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2023Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesiscited by 731
  3. 2016LIFT: Learned Invariant Feature Transformcited by 305
  4. 2017Working hard to know your neighbor's margins: Local descriptor learning losscited by 212
  5. 2023Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Modelscited by 169
  6. 2015EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flowcited by 80
  7. 2018GeoDesc: Learning Local Descriptors by Integrating Geometry Constraintscited by 52
  8. 2016Template Matching Advances and Applications in Image Analysiscited by 7

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