Reconstruction of 3D Shapes from Cross-Sections using Indicator Networks
|A research project by Adrian Briceno Aguilar|
|Conducted at Utrecht University at the Master program|
|Under supervision of Amir Vaxman|
In this work, we propose to tackle the reconstruction of 3D objects from cross-sections with the help of deep learning, focusing on a data-driven approach to reconstruction. Typically, such techniques are used to learn high-level features of 2D images, where their applications range from classification, segmentation to point cloud reconstruction. They can be used in several 3D non-euclidean spaces, such as meshes, point clouds, but one there are no existing definitions for them in the case of cross-sections.
|Reconstruction, cross-sections, binary classification, machine learning|
A concise presentation of the material.