Moving Least-Squares Reconstruction of Large Models with GPUs

作者：Merry Bruce^*; Gain James; Marais Patrick

来源：IEEE Transactions on Visualization and Computer Graphics, 2014, 20(2): 249-261.

DOI：10.1109/TVCG.2013.118

• Moving least squares
• surface reconstruction
• GPU
• out of core

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.

• 出版日期2014-2