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About this paper
Nenad Markuš, Mirko Sužnjević
Researchers at the University of Zagreb developed "gridhopping," an algorithm for efficiently converting Signed Distance Bounds (SDBs) into polygon meshes. This method achieves an O(N^2 log N) computational complexity for an N^3 grid, demonstrating asymptotic speedup over traditional enumeration methods and robust performance with both analytical and neural SDBs.
Key insights
- Leveraging the inherent distance information of SDBs allows for efficient traversal of space, significantly reducing the number of cells that need to be explicitly evaluated for polygonization.
- The combined approach of global ray marching for surface detection and local grid processing for mesh extraction yields an asymptotic complexity of O(N^2 log N), improving upon O(N^3) enumeration.
- A "shrinking factor" applied to learned neural SDBs helps mitigate potential overestimation of distances, ensuring complete and robust mesh reconstruction even with imperfect learned representations.
Summary by alphaXiv.
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