Abstract
Determining connectivity in unstructured point clouds is a long-standing problem that is still not addressed satisfactorily. In this poster, we propose an extension to the proximity graph introduced in [MOW22] to three-dimensional models. We use the spheres-of-influence (SIG) proximity graph restricted to the 3D Delaunay graph to compute connectivity between points. Our approach shows a better encoding of the connectivity in relation to the ground truth than the k-nearest neighborhood (kNN) for a wide range of k values, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., improvements in normal estimation, surface reconstruction, motion planning, simulations, and many more.
Reference
Marin, D., Ohrhallinger, S., & Wimmer, M. (2023). Parameter-Free and Improved Connectivity for Point Clouds. In G. Singh & M. Chu (Eds.), Eurographics 2023 - Posters (pp. 5–6). Eurographics. https://doi.org/10.2312/egp.20231023
