bibtext: @InProceedings{ Wei_Enhanced_MICCAI2024,
                   author = { Wei, Ruofeng and Li, Bin and Chen, Kai and Ma, Yiyao and Liu, Yunhui and Dou, Qi },
                   title = { { Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Scale-aware monocular depth estimation poses a significant challenge in computer-aided endoscopic navigation. However, existing depth estimation methods that do not consider the geometric priors struggle to learn the absolute scale from training with monocular endoscopic sequences. Additionally, conventional methods face difficulties in accurately estimating details on tissue and instruments boundaries. In this paper, we tackle these problems by proposing a novel enhanced scale-aware framework that only uses monocular images with geometric modeling for depth estimation. Specifically, we first propose a multi-resolution depth fusion strategy to enhance the quality of monocular depth estimation. To recover the precise scale between relative depth and real-world values, we further calculate the 3D poses of instruments in the endoscopic scenes by algebraic geometry based on the image-only geometric primitives (i.e., boundaries and tip of instruments). Afterwards, the 3D poses of surgical instruments enable the scale recovery of relative depth maps. By coupling scale factors and relative depth estimation, the scale aware depth of the monocular endoscopic scenes can be estimated. We evaluate the pipeline on in-house endoscopic surgery videos and simulated data. The results demonstrate that our method can learn the absolute scale with geometric modeling and accurately estimate scale-aware depth for monocular scenes. Code is available at: https://github.com/med-air/MonoEndoDepth
title: Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling
authors: Wei, Ruofeng Li, Bin Chen, Kai Ma, Yiyao Liu, Yunhui Dou, Qi