Kavli Affiliate: Michael Wimmer
| First 5 Authors: Markus Schütz, Lukas Herzberger, Michael Wimmer, ,
| Summary:
About: We propose an incremental LOD generation approach for point clouds
that allows us to simultaneously load points from disk, update an octree-based
level-of-detail representation, and render the intermediate results in real
time while additional points are still being loaded from disk. LOD construction
and rendering are both implemented in CUDA and share the GPU’s processing
power, but each incremental update is lightweight enough to leave enough time
to maintain real-time frame rates.
Background: LOD construction is typically implemented as a preprocessing step
that requires users to wait before they are able to view the results in real
time. This approach allows users to view intermediate results right away.
Results: Our approach is able to stream points from an SSD and update the
octree on the GPU at rates of up to 580 million points per second (~9.3GB/s
from a PCIe 5.0 SSD) on an RTX 4090. Depending on the data set, our approach
spends an average of about 1 to 2 ms to incrementally insert 1 million points
into the octree, allowing us to insert several million points per frame into
the LOD structure and render the intermediate results within the same frame.
Discussion/Limitations: We aim to provide near-instant, real-time
visualization of large data sets without preprocessing. Out-of-core processing
of arbitrarily large data sets and color-filtering for higher-quality LODs are
subject to future work.
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