GeoUDF : Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023)
PublisherIEEE
Pages14168-14178
Number of pages11
ISBN (electronic)979-8-3503-0718-4
Publication statusPublished - Oct 2023

Abstract

We present a learning-based method, namely GeoUDF,to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent planes of neighboring points on the surface. Besides,we model the local geometric structure of the input point clouds by explicitly learning a quadratic polynomial foreach point. This not only facilitates upsampling the input sparse point cloud but also naturally induces unoriented normal, which further augments UDF estimation. Finally, to extract triangle meshes from the predicted UDF we propose a customized edge-based marching cube module. We conduct extensive experiments and ablation studies to demonstrate the significant advantages of our methodover state-of-the-art methods in terms of reconstruction accuracy, efficiency, and generality. The source code is publicly available at https://github.com/rsy6318/GeoUDF.

© 2023 IEEE

Citation Format(s)

GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation. / Ren, Siyu; Hou, Junhui; Chen, Xiaodong et al.
Proceedings - 2023 IEEE/CVF International Conference on Computer Vision (ICCV 2023). IEEE, 2023. p. 14168-14178.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review