Register now After registration you will be able to apply for this opportunity online.
Enhancing NeRF predictions by Matching Rendered Images with Nearby References
MOTIVATION
Neural Radiance Fields (NeRFs) require a substantial amount of data from various viewpoints to achieve high-quality reconstructions. This is because NeRFs rely on capturing the intricate details of a scene by learning the light field and volumetric density from multiple angles. Diverse data helps the model understand the scene's geometry, texture, and lighting, allowing it to render detailed and realistic views.
PROPOSAL
We propose to use MaRiNER [Bösiger et al. 2024] as a post-processing step to enhance NeRF reconstructions performed with a smaller amount of data.
Keywords: NeRFs, post-processing, reconstruction
Not specified
Not specified
Requirements: experience with a Python deep learning framework, understanding of 3D scene and camera geometry.
Please send us a CV and transcript.
Dr. Mihai Dusmanu (mihaidusmanu@microsoft.com)
Dr. Zuria Bauer (zbauer@ethz.ch)
Requirements: experience with a Python deep learning framework, understanding of 3D scene and camera geometry.
Please send us a CV and transcript.
Dr. Mihai Dusmanu (mihaidusmanu@microsoft.com) Dr. Zuria Bauer (zbauer@ethz.ch)
Presentación19-6.pdf
