Moving Least-Squares Reconstruction of Large Models with GPUs
Department of Computer Science, University of Cape Town, and South African Centre for High Performance Computing
Transactions on Visualization and Computer Graphics, 2013
@article{merry2013moving,
title={Moving least-squares reconstruction of large models with GPUs},
author={Merry, Bruce and Gain, James and Marais, Patrick},
year={2013},
publisher={IEEE}
}
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.
November 8, 2013 by hgpu
