Learning shape from images

Thesis

This thesis explores how to harness neural networks to learn 3D structure from visual data. Being able to estimate 3D structure is of vital importance in many applications such as VR/AR, medical imaging, and computational photography. Recently, neural networks have been shown to be effective at learning complex functions from data, including in a 3D setting. The central question this thesis addresses is how to overcome the limitations inherent in a strongly supervised application of neural...

Expand abstract

Authors: Wiles, O

• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: self-supervised learning; 3D from X; computer vision; self-supervision; Shape from X; machine learning
• Subjects: Computer Vision; Machine Learning