Data-Parallel Octrees for Surface Reconstruction

Kun Zhou, Minmin Gong, Xin Huang, Baining Guo
State Key Lab. of CAD & CG, Zhejiang Univ., Hangzhou, China
IEEE Transactions on Visualization and Computer Graphics, 2010


   title={Data-parallel octrees for surface reconstruction},

   author={Zhou, K. and Gong, M. and Huang, X. and Guo, B.},

   journal={IEEE Transactions on Visualization and Computer Graphics},


   publisher={Published by the IEEE Computer Society}


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We present the first parallel surface reconstruction algorithm that runs entirely on the GPU. Like existing implicit surface reconstruction methods, our algorithm first builds an octree for the given set of oriented points, then computes an implicit function over the space of the octree, and finally extracts an isosurface as a watertight triangle mesh. A key component of our algorithm is a novel technique for octree construction on the GPU. This technique builds octrees in real time and uses level-order traversals to exploit the fine-grained parallelism of the GPU. Moreover, the technique produces octrees that provide fast access to the neighborhood information of each octree node, which is critical for fast GPU surface reconstruction. With an octree so constructed, our GPU algorithm performs Poisson surface reconstruction, which produces high-quality surfaces through a global optimization. Given a set of 500 K points, our algorithm runs at the rate of about five frames per second, which is over two orders of magnitude faster than previous CPU algorithms. To demonstrate the potential of our algorithm, we propose a user-guided surface reconstruction technique which reduces the topological ambiguity and improves reconstruction results for imperfect scan data. We also show how to use our algorithm to perform on-the-fly conversion from dynamic point clouds to surfaces as well as to reconstruct fluid surfaces for real-time fluid simulation.
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