New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence
Abstract
A fundamental open problem in computer vision-determining pose and correspondence between two sets of points in space(cid:173) is solved with a novel, robust and easily implementable algorithm. The technique works on noisy point sets that may be of unequal sizes and may differ by non-rigid transformations. A 2D varia(cid:173) tion calculates the pose between point sets related by an affine transformation-translation, rotation, scale and shear. A 3D to 3D variation calculates translation and rotation. An objective describ(cid:173) ing the problem is derived from Mean field theory. The objective is minimized with clocked (EM-like) dynamics. Experiments with both handwritten and synthetic data provide empirical evidence for the method.
Cite
Text
Gold et al. "New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence." Neural Information Processing Systems, 1994.Markdown
[Gold et al. "New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/gold1994neurips-new/)BibTeX
@inproceedings{gold1994neurips-new,
title = {{New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence}},
author = {Gold, Steven and Lu, Chien-Ping and Rangarajan, Anand and Pappu, Suguna and Mjolsness, Eric},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {957-964},
url = {https://mlanthology.org/neurips/1994/gold1994neurips-new/}
}