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Moving Least-Squares Reconstruction of Large Models with GPUs
Feb. 2014 (vol. 20 no. 2)
pp. 249-261
Bruce Merry, University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A
James Gain, University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A
Patrick Marais, University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A
Modern laser range scanning campaigns produce extremely large point clouds, and reconstructing a triangulated surface thus requires both out-of-core techniques and significant computational power. We present a GPU-accelerated implementation of the moving least-squares (MLS) surface reconstruction technique. We believe this to be the first GPU-accelerated, out-of-core implementation of surface reconstruction that is suitable for laser range-scanned data. While several previous out-of-core approaches use a sweep-plane approach, we subdivide the space into cubic regions that are processed independently. This independence allows the algorithm to be parallelized using multiple GPUs, either in a single machine or a cluster. It also allows data sets with billions of point samples to be processed on a standard desktop PC. We show that our implementation is an order of magnitude faster than a CPU-based implementation when using a single GPU, and scales well to 8 GPUs.
Index Terms:
Octrees,Surface reconstruction,Graphics processing units,Arrays,Indexes,Surface treatment,Approximation methods,GPU,Octrees,Surface reconstruction,Graphics processing units,Arrays,Indexes,Surface treatment,Approximation methods,out of core,Moving least squares,surface reconstruction
Citation:
Bruce Merry, James Gain, Patrick Marais, "Moving Least-Squares Reconstruction of Large Models with GPUs," IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 2, pp. 249-261, Feb. 2014, doi:10.1109/TVCG.2013.118
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