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Moving Least-Squares Reconstruction of Large Models with GPUs
Feb. 2014 (vol. 20 no. 2)
pp. 249-261
Bruce Merry, Dept. of Comput. Sci., Univ. of Cape Town, Cape Town, South Africa
James Gain, Dept. of Comput. Sci., Univ. of Cape Town, Cape Town, South Africa
Patrick Marais, Dept. of Comput. Sci., Univ. of Cape Town, Cape Town, South Africa
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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