{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/matern-kernels-for-tunable-implicit-surface","title":"Matérn Kernels for Tunable Implicit Surface Reconstruction","arxiv_id":"2409.15466","date":"2024-09-23","proceeding":null,"authors":["Maximilian Weiherer","Bernhard Egger"],"abstract":"We propose to use the family of Mat\\'ern kernels for tunable implicit surface reconstruction, building upon the recent success of kernel methods for 3D reconstruction of oriented point clouds. As we show, both, from a theoretical and practical perspective, Mat\\'ern kernels have some appealing properties which make them particularly well suited for surface reconstruction -- outperforming state-of-the-art methods based on the arc-cosine kernel while being significantly easier to implement, faster to compute, and scaleable. Being stationary, we demonstrate that the Mat\\'ern kernels' spectrum can be tuned in the same fashion as Fourier feature mappings help coordinate-based MLPs to overcome spectral bias. Moreover, we theoretically analyze Mat\\'ern kernel's connection to SIREN networks as well as its relation to previously employed arc-cosine kernels. 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