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
Aug 2019
Thesis

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

Video

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