Deep 3D representation learning
Abstract/Contents
- Abstract
- Among all digital representations we have for real physical objects, 3D is arguably the most expressive encoding. 3D representations allow storage and manipulation of high-level information (e.g. semantics, affordances, function) as well as low-level features (e.g. appearance, materials) about the object. How much we can understand and transform the 3D world is thus largely determined by the performance of algorithms that analyze and create 3D data. While 3D visual computing has predominantly focused on single 3D models or small model collections, the amount of accessible 3D models has increased by several orders of magnitude during the past few years. This significant growth pushes us to redefine 3D visual computing from the perspective of big 3D data. In the thesis, a series of topics on data-driven 3D visual computing will be discussed. These include: constructing an information-rich large-scale 3D model repository, generating synthetic data for supervising neural networks, and learning end-to-end neural networks for analysis and synthesis of 3D geometries. Under the guiding principle of using large-scale 3D data for representation learning, the efforts described in this thesis have led to top-performing algorithms for pure-3D data processing, as well as 3D-assisted semantic, geometric and physical property inference from 2D images. The thesis will conclude by describing several promising directions for future research.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2018; ©2018 |
| Publication date | 2018; 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Su, Hao |
|---|---|
| Degree supervisor | Guibas, Leonidas J |
| Thesis advisor | Guibas, Leonidas J |
| Thesis advisor | Ermon, Stefano |
| Thesis advisor | Savarese, Silvio |
| Thesis advisor | Wetzstein, Gordon |
| Degree committee member | Ermon, Stefano |
| Degree committee member | Savarese, Silvio |
| Degree committee member | Wetzstein, Gordon |
| Associated with | Stanford University, Computer Science Department. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Hao Su. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Hao Su
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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