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We present a learning-based method, namely GeoUDF,to tackle the long-standingand challenging problem of reconstructing a discrete surface from a sparsepoint cloud.To be specific, we propose a geometry-guided learning method forUDF and its gradient estimation that explicitly formulates the unsigneddistance of a query point as the learnable affine averaging of its distances tothe tangent planes of neighboring points on the surface. Besides,we model thelocal geometric structure of the input point clouds by explicitly learning aquadratic polynomial for each point. This not only facilitates upsampling theinput sparse point cloud but also naturally induces unoriented normal, whichfurther augments UDF estimation. Finally, to extract triangle meshes from thepredicted UDF we propose a customized edge-based marching cube module. Weconduct extensive experiments and ablation studies to demonstrate thesignificant advantages of our method over state-of-the-art methods in terms ofreconstruction accuracy, efficiency, and generality. The source code ispublicly available at https://github.com/rsy6318/GeoUDF.

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