Kavli Affiliate: Michael Wimmer
| First 5 Authors: , , , ,
| Summary:
Large-scale terrain scans are the basis for many important tasks, such as
topographic mapping, forestry, agriculture, and infrastructure planning. The
resulting point cloud data sets are so massive in size that even basic tasks
like viewing take hours to days of pre-processing in order to create
level-of-detail structures that allow inspecting the data set in their entirety
in real time. In this paper, we propose a method that is capable of instantly
visualizing massive country-sized scans with hundreds of billions of points.
Upon opening the data set, we first load a sparse subsample of points and
initialize an overview of the entire point cloud, immediately followed by a
surface reconstruction process to generate higher-quality, hole-free
heightmaps. As users start navigating towards a region of interest, we continue
to prioritize the heightmap construction process to the user’s viewpoint. Once
a user zooms in closely, we load the full-resolution point cloud data for that
region and update the corresponding height map textures with the
full-resolution data. As users navigate elsewhere, full-resolution point data
that is no longer needed is unloaded, but the updated heightmap textures are
retained as a form of medium level of detail. Overall, our method constitutes a
form of direct out-of-core rendering for massive point cloud data sets
(terabytes, compressed) that requires no preprocessing and no additional disk
space. Source code, executable, pre-trained model, and dataset are available
at: https://github.com/cg-tuwien/lidarscout
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