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

7 citations · 8 across the 2 of their papers we have counts for

collaborators

7 papers

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

StairNetV3: Depth-aware Stair Modeling using Deep Learning

Chen Wang, Zhongcai Pei, Shuang Qiu +2

Vision-based stair perception can help autonomous mobile robots deal with the challenge of climbing stairs, especially in unfamiliar environments. To address the problem that curre…

cs.CV2023

URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation

Shuwei Shao, Zhongcai Pei, Weihai Chen +3

This work aims to estimate a high-quality depth map from a single RGB image. Due to the lack of depth clues, making full use of the long-range correlation and the local information…