A Streaming Framework for Seamless Building Reconstruction from Large-Scale Aerial LiDAR Data
Abstract
We present a streaming framework for seamless building reconstruction from huge aerial LiDAR point sets. By storing data as stream files on hard disk and using main memory as only a temporary storage for ongoing computation, we achieve efficient out-of-core data management. This gives us the ability to handle data sets with hundreds of millions of points in a uniform manner. By adapting a building modeling pipeline into our streaming framework, we create the whole urban model of Atlanta from 17.7 GB LiDAR data with 683 M points in under 25 hours using less than 1 GB memory. To integrate this complex modeling pipeline with our streaming framework, we develop a state propagation mechanism, and extend current reconstruction algorithms to handle the large scale of data.
Cite
Text
Zhou and Neumann. "A Streaming Framework for Seamless Building Reconstruction from Large-Scale Aerial LiDAR Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206760Markdown
[Zhou and Neumann. "A Streaming Framework for Seamless Building Reconstruction from Large-Scale Aerial LiDAR Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/zhou2009cvpr-streaming/) doi:10.1109/CVPR.2009.5206760BibTeX
@inproceedings{zhou2009cvpr-streaming,
title = {{A Streaming Framework for Seamless Building Reconstruction from Large-Scale Aerial LiDAR Data}},
author = {Zhou, Qian-Yi and Neumann, Ulrich},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2009},
pages = {2759-2766},
doi = {10.1109/CVPR.2009.5206760},
url = {https://mlanthology.org/cvpr/2009/zhou2009cvpr-streaming/}
}