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Two-view multibody structure-and-motion with outliers through model selection.

Konrad Schindler1, David Suter

  • 1Electrical and Computer Systems Engineering Department, Monash University, Clayton Campus, Wellington Road, 3800 VIC, Australia. konrad.schindler@eng.monash.edu.au

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 27, 2006
PubMed
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This study introduces a new method for multi-body structure-and-motion (MSaM) analysis in two-view scenarios. It efficiently identifies multiple moving objects and their motion models from image data, even with noise and outliers.

Area of Science:

  • Computer Vision
  • Robotics
  • 3D Reconstruction

Background:

  • Multibody structure-and-motion (MSaM) involves analyzing multiple views of a 3D scene with independently moving rigid objects.
  • Existing methods often struggle with unknown numbers of objects, uncharacterized measurement noise, and gross outliers.

Purpose of the Study:

  • To develop a robust framework for solving the two-view MSaM problem.
  • To simultaneously determine multiple motion models for moving objects within a scene.
  • To handle unknown measurement noise and outliers effectively.

Main Methods:

  • Utilizes Monte-Carlo sampling to explore potential motion models.
  • Employs rigorous statistical analysis on sampled motion models.
  • Implements simultaneous selection of multiple motion models to explain image measurements.

Related Experiment Videos

  • Adopts a Bayesian framework, allowing flexibility in model selection criteria.

    • Main Results:

    • Successfully identifies an optimal set of motion models for measurements.
    • Demonstrates a method to handle unknown noise and outliers in MSaM.
    • Provides a flexible framework adaptable to different model selection priors.

    Conclusions:

    • The proposed Bayesian framework offers a robust solution for two-view MSaM.
    • The method effectively addresses challenges posed by multiple moving objects and data imperfections.
    • This approach facilitates adaptable motion model selection for various applications.