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Data-driven Keypoint Extractor for Event Data
This project focuses on enhancing camera pose estimation by exploring a data-driven approach to keypoint extraction, leveraging recent advancements in frame-based keypoint extraction techniques.
Neuromorphic cameras, characterized by their robustness to High Dynamic Range (HDR) scenes, high-temporal resolution, and low power consumption, have paved the way for innovative applications in camera pose estimation, particularly for fast motions in challenging environments. This project focuses on enhancing camera pose estimation by exploring a data-driven approach to keypoint extraction, leveraging recent advancements in frame-based keypoint extraction techniques. To achieve this, the project aims to integrate a Visual Odometry (VO) pipeline to provide real-time feedback in an online fashion.
Neuromorphic cameras, characterized by their robustness to High Dynamic Range (HDR) scenes, high-temporal resolution, and low power consumption, have paved the way for innovative applications in camera pose estimation, particularly for fast motions in challenging environments. This project focuses on enhancing camera pose estimation by exploring a data-driven approach to keypoint extraction, leveraging recent advancements in frame-based keypoint extraction techniques. To achieve this, the project aims to integrate a Visual Odometry (VO) pipeline to provide real-time feedback in an online fashion.
The primary objective of this project is to develop a data-driven keypoint extractor capable of identifying interest points in event data. Building upon insights from a previous student project (submitted to CVPR23), participants will harness neural network architectures to extract keypoints within an event stream. Furthermore, the project will involve adapting existing Visual Odometry (VO) algorithms to work with the developed keypoint extractor and tracker. Prospective students should possess prior programming experience in a deep learning framework and have completed at least one course in computer vision. This project offers an exciting opportunity to contribute to the cutting-edge intersection of neuromorphic imaging and computer vision. If you're ready to delve into the realm of data-driven keypoint extraction and its application in camera pose estimation, we're excited to provide further details.
The primary objective of this project is to develop a data-driven keypoint extractor capable of identifying interest points in event data. Building upon insights from a previous student project (submitted to CVPR23), participants will harness neural network architectures to extract keypoints within an event stream. Furthermore, the project will involve adapting existing Visual Odometry (VO) algorithms to work with the developed keypoint extractor and tracker. Prospective students should possess prior programming experience in a deep learning framework and have completed at least one course in computer vision. This project offers an exciting opportunity to contribute to the cutting-edge intersection of neuromorphic imaging and computer vision. If you're ready to delve into the realm of data-driven keypoint extraction and its application in camera pose estimation, we're excited to provide further details.