DGN: Descriptor Generation Network for Feature Matching in Monocular Endoscopy 3D Reconstruction

Endoscopy 3D reconstruction can provide more intuitive perception of the lesions in minimally invasive surgery. The success of 3D reconstruction highly relies on high-quality feature matches between the monocular image pairs, which remains challenging in the textureless endoscopic scenario. In this paper, we propose an effective feature matching framework for monocular endoscopy 3D reconstruction. The framework contains a descriptor generation network (DGN) to generate high-quality feature descriptors in a local-to-global manner, and a local region expansion to fine tune the initial matches obtained from the DGN module. We evaluated our method on the public Hamlyn Centre Laparoscopic/Endoscopic Video Datasets. The experimental results demonstrated that our method can generate sufficient accurate feature matches. Particularly, our method performed better in sparse depth estimation of the endoscopic scenario when compared with the current conventional and deep-learning methods.