UCT CS Research Document Archive

Moving Least-Squares Reconstruction of Large Models with GPUs

Merry, Bruce, James Gain and Patrick Marais (2014) Moving Least-Squares Reconstruction of Large Models with GPUs. IEEE Transactions on Visualization and Computer Graphics 20(2):249-261.

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Abstract

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. 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.

EPrint Type:Journal (Paginated)
Keywords:moving least squares, surface reconstruction, GPU, out of core
Subjects:I Computing Methodologies: I.3 COMPUTER GRAPHICS
ID Code:910
Deposited By:Merry, Bruce
Deposited On:03 September 2014
Alternative Locations:http://doi.ieeecomputersociety.org/10.1109/TVCG.2013.118