In recent years, neural implicit representations gained popularity in 3D reconstruction due to
their expressiveness and flexibility. However, the implicit nature of neural implicit …

Reconstructing a continuous surface from a raw 3D point cloud is a challenging task. Latest
methods employ supervised learning or pretrained priors to learn a signed distance function …

We propose Parametric Gauss Reconstruction (PGR) for surface reconstruction from point
clouds without normals. Our insight builds on the Gauss formula in potential theory, which …

Poisson surface reconstruction (PSR) remains a popular technique for reconstructing
watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness …

We propose a novel method called SHS-Net for oriented normal estimation of point clouds
by learning signed hyper surfaces, which can accurately predict normals with global …

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero
level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and …

Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …

We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm
for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we …