Extremely Dense Point Correspondences using a Learned Feature Descriptor

1 code implementation • CVPR 2020 • Xingtong Liu, Yiping Zheng, Benjamin Killeen, Masaru Ishii, Gregory D. Hager, Russell H. Taylor, Mathias Unberath

In direct comparison to recent local and dense descriptors on an in-house sinus endoscopy dataset, we demonstrate that our proposed dense descriptor can generalize to unseen patients and scopes, thereby largely improving the performance of Structure from Motion (SfM) in terms of model density and completeness.

3D Reconstruction Anatomy +2