Register now After registration you will be able to apply for this opportunity online.
3D Surface Reconstruction from Sparse Viewpoints for Medical Education and Surgical Navigation
In medical education and surgical navigation, achieving accurate multi-view 3D surface
reconstruction from sparse viewpoints is a critical challenge. This Master's thesis
addresses this problem by first computing normal and optionally reflectance maps for
each viewpoint, and then fusing this data to obtain the geometry of the scene and,
optionally, its reflectance.
The research explores multiple techniques for normal map computation, including
photometric stereo, data-driven methods, and stereo matching, either individually or in
combination.
The outcomes of this study aim to pave the way for the creation of highly realistic and
accurate 3D models of surgical fields and anatomical structures. These models have
the potential to significantly improve medical education by providing detailed and
interactive representations for learning. Additionally, in the context of surgical
navigation, these advancements can enhance the accuracy and effectiveness of
surgical procedures.
References:
Yu, Zehao, Peng, Songyou, Niemeyer, Michael, Sattler, Torsten, Geiger, Andreas.
MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface
Reconstruction. NeurIPS 2022.
Baptiste Brument and Robin Bruneau and Yvain Quéau and Jean Mélou and François
Lauze and Jean-Denis Durou and Lilian Calvet. RNb-Neus: Reflectance and normal
Based reconstruction with NeuS. CVPR 2024.
Gwangbin Bae and Andrew J. Davison. Rethinking Inductive Biases for Surface Normal
Estimation. CVPR 2024.
Keywords: 3D reconstruction
Not specified
Not specified
Lilian Calvet (Lilian.Calvet@balgrist.ch), Jonas Hein (jonas.hein@inf.ethz.ch)
Lilian Calvet (Lilian.Calvet@balgrist.ch), Jonas Hein (jonas.hein@inf.ethz.ch)
3D Surface Reconstruction from Sparse Viewpoints.pdf
