Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation.

We propose a novel method called SHS-Net for point cloud normal estimation by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods estimate oriented normals through a two-stage pipeline, i.e.

Fast Learning Radiance Fields by Shooting Much Fewer Rays.

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods.

Review of light field technologies.

Light fields are vector functions that map the geometry of light rays to the corresponding plenoptic attributes. They describe the holographic information of scenes by representing the amount of light flowing in every direction through every point in space. The physical concept of light fields was first proposed in 1936, and light fields are becoming increasingly important in the field of computer graphics, especially with the fast growth of computing capacity as well as network bandwidth.

Slicing and support structure generation for 3D printing directly on B-rep models.

Traditional 3D printing is based on stereolithography or standard tessellation language models, which contain many redundant data and have low precision. This paper proposes a slicing and support structure generation algorithm for 3D printing directly on boundary representation (B-rep) models. First, surface slicing is performed by efficiently computing the intersection curves between the faces of the B-rep models and each slicing plane.