Modeling virtual environments 3D assets from visual inputs
Abstract/Contents
- Abstract
- Modeling the physical world is crucial for many applications that significantly impact society, includ- ing robotics, autonomous vehicles, virtual and augmented reality (AR/VR). Research works com- monly leverage 3D virtual environments as a proxy for real environments to perform experiments. By using virtual environments, experiments like robotic simulations, navigation, and interactions can be performed safely and at a lower cost compared to real-world environments. 3D geometric and material assets are the building blocks of virtual environments. However the process of creating 3D assets from scratch is mostly manual, time-consuming, and requires significant expertise. This challenge motivates the development of automated methods for 3D assets generation. We introduce a Computer Vision framework for generating 3D assets from visual inputs that include images and 3D point clouds from real scenes. Visual inputs from a scene are parsed into individual objects, structures, and materials which can be processed into 3D assets. This framework involves four key tasks: 3D semantic segmentation, shape completion, shape modeling, and material prediction. We introduce representations, algorithms, machine learning models, and datasets to address the chal- lenges associated with each of these tasks. We introduces new models for 3D semantic segmentation and shape completion. It also introduces a graph representation and search algorithm that facilitates generating multiple variations of a given shape. Last, it contributes a large scale dataset of procedu- ral materials along with a neural network model that generates procedural materials from images. The results of this thesis is a set of methods addressing the aforementioned tasks and supporting the creation of realistic virtual environments at scale.
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 | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tchapmi Petse, Lyne |
|---|---|
| Degree supervisor | Savarese, Silvio |
| Thesis advisor | Savarese, Silvio |
| Thesis advisor | Bambos, Nicholas |
| Thesis advisor | Pauly, John (John M.) |
| Degree committee member | Bambos, Nicholas |
| Degree committee member | Pauly, John (John M.) |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Lyne P. Tchapmi. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/vx419pw4129 |
Access conditions
- Copyright
- © 2023 by Lyne Tchapmi Petse
- License
- This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 3.0 Unported license (CC BY-NC-ND).
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