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Multimodal Fusion for Enhanced Neural Scene Reconstruction Quality
The project aims to explore how prior 3D information can assist in reconstructing fine details in NeRFs and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion.
Recent advancements in neural radiance field training have shown remarkable success by fusing together vision and semantic modalities for improved reconstruction quality. In this project, we build upon this recent trend and investigate how the use of modalities such as depth and event data can improve radiance fields. The project aims to explore how prior 3D information can assist in reconstructing fine details and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion. By exploring the fusion of these modalities, we aim to achieve more accurate and detailed representations of complex environments.
Recent advancements in neural radiance field training have shown remarkable success by fusing together vision and semantic modalities for improved reconstruction quality. In this project, we build upon this recent trend and investigate how the use of modalities such as depth and event data can improve radiance fields. The project aims to explore how prior 3D information can assist in reconstructing fine details and how the help of high-temporal resolution data can enhance modeling in the case of scene and camera motion. By exploring the fusion of these modalities, we aim to achieve more accurate and detailed representations of complex environments.
The primary goal of this project is to evaluate the fusion of multiple sensor modalities, including RGB, depth, and event cameras, for enhanced scene reconstruction quality. We aim to leverage the unique strengths of each modality to achieve finer detail reconstruction and effectively handle complex scenes. Applicants with a background in programming (Python/Matlab), experience in computer vision, and familiarity with machine learning frameworks (PyTorch) are encouraged to apply.
The primary goal of this project is to evaluate the fusion of multiple sensor modalities, including RGB, depth, and event cameras, for enhanced scene reconstruction quality. We aim to leverage the unique strengths of each modality to achieve finer detail reconstruction and effectively handle complex scenes. Applicants with a background in programming (Python/Matlab), experience in 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)