Automatic video to point cloud registration in a structure-from-motion framework

Vidal, E.; Piotto, N.; Cordara, G.; Burgos, F.M.

doi:10.1109/ICIP.2015.7351282

by E. Vidal, N. Piotto, G. Cordara, F.M. Burgos

Abstract:

In Structure-from-Motion (SfM) applications, the capability of integrating new visual information into existing 3D models is an important need. In particular, video streams could bring significant advantages, since they provide dense and redundant information, even if normally only relative to a limited portion of the scene. In this work we propose a fast technique to reliably integrate local but dense information from videos into existing global but sparse 3D models. We show how to extract from the video data local 3D information that can be easily processed allowing incremental growing, refinement, and update of the existing 3D models. The proposed technique has been tested against two state-of-the-art SfM algorithms, showing significant improvements in terms of computational time and final point cloud density.

Reference:

E. Vidal, N. Piotto, G. Cordara, F.M. Burgos, "Automatic video to point cloud registration in a structure-from-motion framework", In Image Processing (ICIP), 2015 IEEE International Conference on, pp. 2646-2650, 2015.

Bibtex Entry:

@INPROCEEDINGS{7351282, 
author={Vidal, E. and Piotto, N. and Cordara, G. and Burgos, F.M.}, 
booktitle={Image Processing (ICIP), 2015 IEEE International Conference on}, 
title={Automatic video to point cloud registration in a structure-from-motion framework}, 
year={2015}, 
pages={2646-2650}, 
abstract={In Structure-from-Motion (SfM) applications, the capability of integrating new visual information into existing 3D models is an important need. In particular, video streams could bring significant advantages, since they provide dense and redundant information, even if normally only relative to a limited portion of the scene. In this work we propose a fast technique to reliably integrate local but dense information from videos into existing global but sparse 3D models. We show how to extract from the video data local 3D information that can be easily processed allowing incremental growing, refinement, and update of the existing 3D models. The proposed technique has been tested against two state-of-the-art SfM algorithms, showing significant improvements in terms of computational time and final point cloud density.}, 
keywords={image registration;solid modelling;video signal processing;3D models;SfM applications;automatic video to point cloud registration;structure-from-motion framework;video data local 3D information;video streams;visual information;Cameras;Computational modeling;Feature extraction;Iterative closest point algorithm;Solid modeling;Streaming media;Three-dimensional displays;3D Reconstruction;Point Cloud Alignment;SfM;Video Registration}, 
doi={10.1109/ICIP.2015.7351282}, 
month={Sept},}