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<div class="csl-entry">Erler, P., Herzberger, L., Wimmer, M., & Schütz, M. (2025). LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds. In <i>High-Performance Graphics 2025</i>. High-Performance Graphics 2025, Koppenhagen, Denmark. the Eurographics Association. https://doi.org/10.2312/hpg.20251170</div>
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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
en
dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.subject
Point-based models
en
dc.subject
Mesh models
en
dc.subject
Neural networks
en
dc.subject
Reconstruction
en
dc.subject
Aerial LIDAR
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dc.title
LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.publication
High-Performance Graphics 2025
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dc.relation.isbn
978-3-03868-291-2
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dc.relation.issn
2079-8687
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dc.relation.grantno
ICT22-55
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
High-Performance Graphics 2025 - Symposium Papers
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tuw.peerreviewed
true
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tuw.relation.publisher
the Eurographics Association
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tuw.relation.publisherplace
deutschland
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tuw.project.title
Instant Visualization and Interaction for Large Point Clouds