Uncertainty-aware 3D Reconsturction for Synthetic Environment
Abstract
Proposed Title: Uncertainty-aware 3D photogrammetric reconstruction for synthetic environmentAbstract: Synthetic Environments (SE) aim to replicate the real-world scenario to a degree that simulated activities can accurately predict the potential engagement in the field, such that decisions made in SE can be adapted in real-world practices. Typically, field-level geo-specific models are generated through image-based photogrammetric approaches, where errors propagated through typical photogrammetric processes may avoidably impair the geometric fidelity the models, while these errors remain uncharacterized in synthetic environment. In addition, to create realistic simulations, SE needs to know detailed object categories such as light poles, traffic signs, and bare terrains, to model the respective effects for immersive experiences. These applications require the high-resolution 3D geo-specific data to be accuracy-wise characterizable, semantically resolvable, and photo-realistically renderable when being incorporated into SE simulators. This project aims to develop a set of fundamental techniques to improve the error characterization, semantic resolution, and realism ofgeo-specific 3D photogrammetric models for synthetic environment, to introduce an uncertainty-aware geo-specific 3D asset creation paradigm for enhance the fidelity of virtual training and rehearsal.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 24, 2023
- Source ID
- N000142312670
Entities
People
- Rongjun Qin
Organizations
- Office of Naval Research
- Ohio State University
- United States Navy