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One-dimensional dense disparity estimation for three-dimensional reconstruction.

Lionel Oisel1, Etienne Mémin, Luce Morin

  • 1IRISA, Campus de Beaulieu, 35042 Rennes, France.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces a novel method for automatic 3D reconstruction using two weakly calibrated images. The approach models complex scenes by creating and refining 2D triangular meshes into 3D surfaces for VRML output.

Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Photogrammetry

Background:

  • 3D reconstruction from images is crucial for various applications.
  • Existing methods often require precise calibration or struggle with complex scenes.
  • Weakly calibrated image pairs present a significant challenge for accurate 3D modeling.

Purpose of the Study:

  • To develop a fully automatic method for 3D reconstruction of complex rigid scenes from weakly calibrated image pairs.
  • To enable the creation of detailed 3D models compatible with VRML.
  • To overcome limitations of existing reconstruction techniques.

Main Methods:

  • A two-step algorithm combining sparse matching and dense motion estimation to generate a 2D triangular mesh.
  • Iterative refinement of the 2D mesh to fit arbitrary 3D surfaces, with each patch representing a 3D plane.

Related Experiment Videos

  • Dense disparity field estimation constrained by epipolar geometry, followed by segmentation using homographic models and Delaunay triangulation.
  • Integration with weak calibration and camera motion estimation for final 3D model generation.

    • Main Results:

    • Successful generation of a 2D planar model from a dense disparity field.
    • Iterative mesh refinement leads to accurate fitting of 3D surfaces.
    • The method produces a VRML-compatible 3D model from weakly calibrated images.
    • Demonstrated robustness in handling complex rigid scenes.

    Conclusions:

    • The proposed method offers a fully automatic and robust solution for 3D reconstruction from weakly calibrated images.
    • It effectively models complex rigid scenes by leveraging 2D mesh refinement and geometric constraints.
    • This approach advances the field of 3D modeling by enabling detailed reconstruction from less constrained input data.