most citedSelf-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the Rescue

7 citations · 7 across the 1 of their papers we have counts for

collaborators

9 papers

cs.CV2024

: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis

Xiaotong Guo, Huijie Zhao, Shuwei Shao +2

Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised me…

cs.CV2023

MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Model

Shuwei Shao, Zhongcai Pei, Weihai Chen +3

Over the past few years, self-supervised monocular depth estimation that does not depend on ground-truth during the training phase has received widespread attention. Most efforts f…

cs.CV2023

NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion

Shuwei Shao, Zhongcai Pei, Weihai Chen +2

Over the past few years, monocular depth estimation and completion have been paid more and more attention from the computer vision community because of their widespread application…

cs.CV202321 cited

IEBins: Iterative Elastic Bins for Monocular Depth Estimation

Shuwei Shao, Zhongcai Pei, Xingming Wu +3

Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications. Recently, several methods reframe the MD…

cs.CV2023

NDDepth: Normal-Distance Assisted Monocular Depth Estimation

Shuwei Shao, Zhongcai Pei, Weihai Chen +2

Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep…

cs.CV2023

A geometry-aware deep network for depth estimation in monocular endoscopy

Yongming Yang, Shuwei Shao, Tao Yang +4

Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the importan…