title: Moving least-squares reconstruction of large models with GPUs.
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. 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.
authors.affiliation: University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A.
authors.forename: Bruce
authors.initials: B
authors.lastname: Merry
authors.affiliation: University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A.
authors.forename: James
authors.initials: J
authors.lastname: Gain
authors.affiliation: University of Cape Town Centre and South African Centre for High Performance Computing, Cape Town N/A.
authors.forename: Patrick
authors.initials: P
authors.lastname: Marais