Distributed Poisson Surface Reconstruction

Screened Poisson surface reconstruction robustly creates meshes from oriented point sets. For large datasets, the technique requires hours of computation and significant memory. We present a method to parallelize and distribute this computation over multiple commodity client nodes. The method partitions space on one axis into adaptively sized slabs containing balanced subsets of points. Because the Poisson formulation involves a global system, the challenge is to maintain seamless consistency at the slab boundaries and obtain a reconstruction that is indistinguishable from the serial result. To this end, we express the reconstructed indicator function as a sum of a low-resolution term computed on a server and high-resolution terms computed on distributed clients. Using a client-server architecture, we map the computation onto a sequence of serial server tasks and parallel client tasks, separated by synchronization barriers. This architecture also enables low-memory evaluation on a single computer, albeit without speedup. We demonstrate a 700 million vertex reconstruction of the billion point David statue scan in less than 20 min on a 65-node cluster with a maximum memory usage of 45 GB/node, or in 14 h on a single node.