Project Description
Image-based 3D reconstruction is a huge topic in machine learning today, with applications ranging from autonomous driving to augmented reality. While large datasets for training these machine learning systems do exist, they are designed to be general and are not easily tailored to the specific learning task of interest. In this project, students will use the Unity game engine to design tools for generating and visualizing scene-level training data for image-based 3D reconstruction tasks, aiming to make the tools generalizable to any existing 3D dataset. Using these tools, students will generate training data using a variety of available 3D datasets and explore training machine learning systems on the single-view, multi-view, or video-based reconstruction tasks.
Team Members
- Pranav Acharya (pranavacharya@ucsb.edu)
- Maya Ha (mayaha@umail.ucsb.edu)
- Vivian Ross (vivianross@ucsb.edu)
- Daniel Lohn (dlohn@ucsb.edu)
Professor and Mentors
- Professor: Prof. Tobias Hollerer (holl@cs.ucsb.edu)
- Mentors: Noah Stier (noahstier@ucsb.edu), Alex Rich (anrich@ucsb.edu), Ehsan Sayyed (esayyad@ucsb.edu)
Meeting Time
- Meeting with Prof. Mirza and Prof. Eiers
- Location: Zoom
- Time: Th, 2:30-3pm
Links to Proposals and Presentation
- Proposal link
- Final presentation:
Individual Logs
Peer Review
- Alex's Review, Annotated Proposal
- Jake Miller's Review
- Qiru's Review
- Kunal's Review, Annotated Proposal