Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Representing shapes as level sets of neural networks has been recently proved to be useful
for different shape analysis and reconstruction tasks. So far, such representations were …

We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel
views of a 3D scene with quality that fools the human eye. Yet, generating these images is …

Learning 3D geometry directly from raw data, such as point clouds, triangle soups, or
unoriented meshes is still a challenging task that feeds many downstream computer vision …

Abstract We present Neural Kernel Fields: a novel method for reconstructing implicit 3D
shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art …

Implicit surface representations, such as signed-distance functions, combined with deep
learning have led to impressive models which can represent detailed shapes of objects with …

Deep implicit functions have shown remarkable shape modeling ability in various 3D
computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as …

In stark contrast to the case of images, finding a concise, learnable discrete representation of
3D surfaces remains a challenge. In particular, while polygon meshes are arguably the most …

Abstract We present Neural Splines, a technique for 3D surface reconstruction that is based
on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method …