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Efficient Neural Scene Reconstruction with Event Cameras
This project seeks to leverage the sparse nature of events to accelerate the training of radiance fields.
Building upon the success of learning-based methods in scene reconstruction and synthesis, this project aims to advance the field forward by enhancing the efficiency and speed of existing formulations in the context of event cameras. While learning-based methods have already showcased the potential of event cameras in neural scene reconstruction, they often require extensive training to achieve top-quality results. This project seeks to address this limitation by leveraging the sparse nature of events to accelerate the training of radiance fields.
Building upon the success of learning-based methods in scene reconstruction and synthesis, this project aims to advance the field forward by enhancing the efficiency and speed of existing formulations in the context of event cameras. While learning-based methods have already showcased the potential of event cameras in neural scene reconstruction, they often require extensive training to achieve top-quality results. This project seeks to address this limitation by leveraging the sparse nature of events to accelerate the training of radiance fields.
The primary objective of this project is to explore innovative strategies for neural scene reconstruction using event cameras, with a focus on optimizing the training and inference speed. Applicants with a background in programming (Python/Matlab), computer vision, and familiarity with machine learning frameworks (PyTorch) are encouraged to apply.
The primary objective of this project is to explore innovative strategies for neural scene reconstruction using event cameras, with a focus on optimizing the training and inference speed. Applicants with a background in programming (Python/Matlab), computer vision, and familiarity with machine learning frameworks (PyTorch) are encouraged to apply.
Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch) and Manasi Muglikar (muglikar AT ifi DOT uzh DOT ch)
Interested candidates should send their CV, transcripts (bachelor and master), and descriptions of relevant projects to Marco Cannici (cannici AT ifi DOT uzh DOT ch) and Manasi Muglikar (muglikar AT ifi DOT uzh DOT ch)