Improve 3D surface reconstruction from point clouds using geometry in distance function with GeoUDF

Improve 3D surface reconstruction from point clouds using geometry in distance function with GeoUDF

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

arXiv paper abstract https://arxiv.org/abs/2211.16762

arXiv PDF paper https://arxiv.org/pdf/2211.16762.pdf

GitHub https://github.com/rsy6318/geoudf

The recent neural implicit representation-based methods have greatly advanced ... solving the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.

These methods generally learn either a binary occupancy or signed/unsigned distance field (SDF/UDF) as surface representation.

However, all the existing SDF/UDF-based methods use neural networks to implicitly regress the distance in a purely data-driven manner, thus limiting the accuracy and generalizability

... propose the first geometry-guided 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 neighbouring points.

... model the local geometric structure of the input point clouds by explicitly learning a quadratic polynomial for each point.

... demonstrate the significant advantages of ... method over state-of-the-art methods in terms of reconstruction accuracy, efficiency, and generalizability ...

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