New paper: Variational Shape Reconstruction via Quadric Error Metrics

Published on May 26, 2023 Updated on May 30, 2023

Discover this new paper on clustering methods to which Pierre Alliez (3IA chairholder) and Tong Zhao (3IA PhD student) have contributed.

A new clustering method for surface reconstruction from 3D point clouds. To appear at the ACM SIGGRAPH 2023 conference, in August 2023.

Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the input points enriched with quadric error metrics, where the generator of each cluster is the optimal 3D point for the sum of its quadric error metrics. This approach favors the placement of generators on sharp features, and tends to equidistribute the error among clusters. We reconstruct the output surface mesh from the adjacency between clusters and a constrained binary solver. We combine our clustering process with an adaptive refinement driven by the error. Compared to prior art, our method avoids dense re- construction prior to simplification and produces immediately an optimized mesh.