{"ID":2844025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07206","arxiv_id":"2511.07206","title":"Geometric implicit neural representations for signed distance functions","abstract":"\\textit{Implicit neural representations} (INRs) have emerged as a promising framework for representing signals in low-dimensional spaces. This survey reviews the existing literature on the specialized INR problem of approximating \\textit{signed distance functions} (SDFs) for surface scenes, using either oriented point clouds or a set of posed images. We refer to neural SDFs that incorporate differential geometry tools, such as normals and curvatures, in their loss functions as \\textit{geometric} INRs. The key idea behind this 3D reconstruction approach is to include additional \\textit{regularization} terms in the loss function, ensuring that the INR satisfies certain global properties that the function should hold -- such as having unit gradient in the case of SDFs. We explore key methodological components, including the definition of INR, the construction of geometric loss functions, and sampling schemes from a differential geometry perspective. Our review highlights the significant advancements enabled by geometric INRs in surface reconstruction from oriented point clouds and posed images.","short_abstract":"\\textit{Implicit neural representations} (INRs) have emerged as a promising framework for representing signals in low-dimensional spaces. This survey reviews the existing literature on the specialized INR problem of approximating \\textit{signed distance functions} (SDFs) for surface scenes, using either oriented point...","url_abs":"https://arxiv.org/abs/2511.07206","url_pdf":"https://arxiv.org/pdf/2511.07206v1","authors":"[\"Luiz Schirmer\",\"Tiago Novello\",\"Vinícius da Silva\",\"Guilherme Schardong\",\"Daniel Perazzo\",\"Hélio Lopes\",\"Nuno Gonçalves\",\"Luiz Velho\"]","published":"2025-11-10T15:33:02Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CG\",\"cs.GR\"]","methods":"[]","has_code":false}