Propagation of innovative information in non-linear least-squares structure from motion
- 13 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 223-229
- https://doi.org/10.1109/iccv.2001.937628
Abstract
We present a new technique that improves upon existing structure from motion (SFM) methods. We propose a SFM algorithm that is both recursive and optimal. Our method incorporates innovative information from new frames into an existing solution without optimizing every camera pose and scene structure parameter. To do this, we incrementally optimize larger subsets of parameters until the error is minimized. These additional parameters are included in the optimization by tracing connections between points and frames. In many cases, the complexity of adding a frame is much smaller than full bundle adjustment of all the parameters. Our algorithm is best described us incremental bundle adjustment as it allows new information to be added to art existing non-linear least-squares solution.Keywords
This publication has 9 references indexed in Scilit:
- A batch/recursive algorithm for 3D scene reconstructionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Reducing "Structure from Motion": a general framework for dynamic vision. 2. Implementation and experimental assessmentPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Robust parameterization and computation of the trifocal tensorImage and Vision Computing, 1997
- In defense of the eight-point algorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Sequential Updating of Projective and Affine Structure from MotionInternational Journal of Computer Vision, 1997
- Recursive estimation of motion, structure, and focal lengthPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1995
- Recovering 3D Shape and Motion from Image Streams Using Nonlinear Least SquaresJournal of Visual Communication and Image Representation, 1994
- Recursive 3-D motion estimation from a monocular image sequenceIEEE Transactions on Aerospace and Electronic Systems, 1990
- Kalman filter-based algorithms for estimating depth from image sequencesInternational Journal of Computer Vision, 1989