Reconstruction of Scene Models from Sparse 3D Structure
Abstract
In this paper we present a geometric theory for reconstruction of surface models from sparse 3D data captured from N camera views which are consistent with the data visibility. Sparse 3D measurements of real scenes are readily estimated from image sequences using structure-from-motion techniques. Currently there is no general method for reconstruction of 3D models of arbitrary scenes from sparse data. We introduce an algorithm for recursive integration of sparse 3D structure to obtain a consistent model. This algorithm is shown to converge to the real scene structure as the number of views increases and to have a computational cost which is linear in the number of views. Results are presented for real and synthetic image sequences which demonstrate correct reconstruction for scenes containing significant occlusions.
Cite
Text
Manessis et al. "Reconstruction of Scene Models from Sparse 3D Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000. doi:10.1109/CVPR.2000.854938Markdown
[Manessis et al. "Reconstruction of Scene Models from Sparse 3D Structure." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000.](https://mlanthology.org/cvpr/2000/manessis2000cvpr-reconstruction/) doi:10.1109/CVPR.2000.854938BibTeX
@inproceedings{manessis2000cvpr-reconstruction,
title = {{Reconstruction of Scene Models from Sparse 3D Structure}},
author = {Manessis, Anastasios and Hilton, Adrian and Palmer, Phil and McLauchlan, Philip F. and Shen, Xinquan},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2000},
pages = {2666-2673},
doi = {10.1109/CVPR.2000.854938},
url = {https://mlanthology.org/cvpr/2000/manessis2000cvpr-reconstruction/}
}