Deim Seminar
Title
Hybrid Deep Learning Architectures for Robust End to End Camera Localization
Conferenciant
Hussein Hasan Hameed
Professor/a organitzador/a
Oriol Farràs
Institution
URV
Date
11-12-2024 10:45
Summary
Abstract: Camera localization plays a pivotal role in robotics, autonomous systems, and augmented reality, providing accurate estimates of a camera's position and orientation within a 3D environment. Traditional feature-based methods rely on keypoint detection and matching but struggle in low-texture or dynamic environments. Image retrieval-based approaches can localize efficiently in large-scale scenes but lack precision in fine-grained pose estimation. Geometry-based methods, such as structure-from-motion, offer high accuracy but are computationally intensive and depend heavily on high-quality input data. These limitations have driven the exploration of hybrid deep learning models to overcome the challenges of scalability, robustness, and adaptability.
Recent advancements in deep learning have shown promise in addressing these limitations. By combining ideas from feature extraction and global context modeling, modern architectures explore ways to enhance the precision and reliability of camera localization systems. This seminar focuses on the general objectives of advancing camera localization technology, reflecting on existing methodologies and the potential directions for future research. Through a review of related work and insights from the field, we aim to provide an overview of how emerging technologies can redefine the landscape of camera localization for next-generation applications.
Place
Lab 231
Language
Anglès
