Latest Research Project
A Streaming Framework for Seamless Building Reconstruction from Large-Scale Aerial LiDAR Data
IEEE CVPR 2009. (Paper, Video, Poster)
Qian-Yi Zhou and Ulrich Neumann
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 17.7GB LiDAR data
with 683M points in under 25 hours using less than 1GB
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.
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