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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry
06:36

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

Published on: April 15, 2021

Pose-free structure from motion using depth from motion constraints.

Ji Zhang1, Mireille Boutin, Daniel G Aliaga

  • 1Department of Mathematics, Purdue University, West Lafayette, IN 47906, USA. jeffrey_zhangji@yahoo.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 28, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for Structure from Motion (SFM) by eliminating camera pose variables. This approach improves conditioning and accuracy in 3D scene reconstruction from images.

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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Computer Vision
  • Robotics
  • 3D Reconstruction

Background:

  • Structure from Motion (SFM) reconstructs 3D scenes from images but is often ill-conditioned due to unknown camera poses.
  • Existing methods track features and estimate 3D positions and camera poses simultaneously.
  • Recovering camera pose is a significant challenge, impacting the accuracy of SFM.

Purpose of the Study:

  • To propose an alternative formulation of the SFM problem that removes external camera pose parameters.
  • To develop a more robust and accurate method for 3D scene geometry recovery.
  • To improve the conditioning of the SFM problem and reduce the number of variables.

Main Methods:

  • Employs algebraic variable elimination to reformulate the standard SFM equations.
  • Eliminates camera orientations, camera center positions, and 3D point coordinates, retaining only depths.
  • Utilizes homotopy methods for global solutions of the resulting 'depth-only equations' in small cases.

Main Results:

  • Achieved a better-conditioned SFM problem with significantly fewer variables.
  • Developed 'depth-only equations' that can be solved globally using homotopy methods for limited data.
  • Formulated a pose-free cost function for refining SFM solutions, outperforming bundle adjustment in accuracy.

Conclusions:

  • The proposed algebraic formulation offers a more stable and accurate approach to Structure from Motion.
  • Depth-only equations provide a novel way to solve and refine SFM problems.
  • The method shows potential for improved 3D reconstruction accuracy compared to traditional techniques.