Generative adversarial modeling of three-dimensional shapes

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 53-59).Given a 3D shape, humans are capable of telling whether it looks natural. This shape priors, namely the perception of whether a shape looks realistic, are formed over years of our interactions with surrounding 3D objects, and go beyond simple definition of objects. In this thesis, we propose two models, 3D Generative Adversarial Network and ShapeHD, to learn shape priors from existing 3D shapes via generative-adversarial modeling, pushing the limits of shape generation, single-view shape completion and reconstruction. For shape generation, we demonstrate that our 3D-GAN generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods; for single-view shape completion and reconstruction, we show that ShapeHD recovers fine details for 3D shapes, and outperforms state-of-the-art by a large margin on both tasks.by Chengkai Zhang.M. Eng