Fast 3D modeling from video

@article{Aguiar1999Fast3M,
  title={Fast 3D modeling from video},
  author={Pedro M. Q. Aguiar and Jos{\'e} M. F. Moura},
  journal={1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451)},
  year={1999},
  pages={289-294}
}
  • P. Aguiar, José M. F. Moura
  • Published 1999
  • Computer Science
  • 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451)
We build 3D models of rigid bodies from video sequences. The algorithm we use is simple and robust. It recovers the 3D shape parameters and the 3D motion parameters by first estimating the parameters of the induced optical flow representation. To estimate the 3D shape and 3D motion from the optical flow, we use a fast algorithm that is based on the factorization of a matrix that is rank 1 in a noiseless situation. We demonstrate our approach with a piecewise planar object shape built from a… 

Figures from this paper

Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video

TLDR
A novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2- D features to3-D in the face of projection and foreshortening effects to encourage its applicability in 3- D reconstruction of other rigid objects in video.

Factorization with missing data for 3D structure recovery

TLDR
This paper makes an experimental analysis of the algorithms of R.F.Q. Guerreiro and P.M.

References

SHOWING 1-7 OF 7 REFERENCES

Video representation via 3D shaped mosaics

  • P. AguiarJosé M. F. Moura
  • Mathematics
    Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269)
  • 1998
TLDR
This work generalizes to 3D shaped mosaics the generative video representation of video sequences introduced by Jasinschi and Moura using a parametric representation of the 3D shape and 3D motions from the 2D motions in the video sequence.

A fast algorithm for rigid structure from image sequences

TLDR
This paper develops a new algorithm that has two relevant advantages over the previous algorithms, instead of imposing a common origin for the parametric representation of the 3D surface patches, it allows the the specification of different origins for different patches.

Factorization as a rank 1 problem

  • P. AguiarJosé M. F. Moura
  • Computer Science
    Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149)
  • 1999
TLDR
This paper reformulates the factorization method for recovering 3D structure from 2D video using the fact that the x and y coordinates of each feature are known from their projection onto the image plane in frame 1, and shows how to compute the 3D shape and 3D motion by a simple factorization of a matrix that is rank 1 in a noiseless situation.

A Paraperspective Factorization Method for Shape and Motion Recovery

TLDR
This work has shown that the paraperspective factorization method can be applied to a much wider range of motion scenarios, including image sequences containing motion toward the camera and aerial image sequences of terrain taken from a low-altitude airplane.

Video representation with three-dimensional entities

TLDR
A novel content-based video representation using tridimensional entities: textured object models and pose estimates and it provides alternative means for handling video by manipulating and compositing three-dimensional (3-D) entities.

Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces

TLDR
It is shown that seven point correspondences are sufficient to uniquely determine from two perspective views the three-dimensional motion parameters (within a scale factor for the translations) of a rigid object with curved surfaces.

Hierarchical Model-Based Motion Estimation

TLDR
This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-fine refinement strategy.