3D City-scape Reconstruction from Motion
Reconstructing dense models of real-world 3D scenes is important for autonomous driving tasks. However, motion estimation for an agile single camera moving through general, unknown scenes has proved to be a challenging problem. The task becomes much more challenging in autonomous driving when real-time performance is required under disturbance of transient change of moving objects (e.g. vehicles, pedestrians) and surrounding environments (e.g. lighting conditions). The goal of this project is to build a pipeline for processing driving videos gathered by on-dash car camera on top of existing Structure from Motion method. This pipeline should generate high-quality reconstruction model of the environment using previously unknown scene along with mapping the trajectory of the 3D camera pose as output. The reconstructed vision system should ideally enable localization, general spatial awareness and scene understanding, opens up new possibilities for learning of autonomous driving policies.
Message to Sponsor
- Major: Computer Science
- Sponsor: Pergo L&S
- Mentor: Fisher Yu