Zobrazeno 1 - 10
of 217
pro vyhledávání: '"Pei, Zhongcai"'
The distribution shift of electroencephalography (EEG) data causes poor generalization of braincomputer interfaces (BCIs) in unseen domains. Some methods try to tackle this challenge by collecting a portion of user data for calibration. However, it i
Externí odkaz:
http://arxiv.org/abs/2405.11163
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 focus on designing different types of network architectures and loss fu
Externí odkaz:
http://arxiv.org/abs/2311.07198
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 applications. In this paper, we introduce novel physics (geometry)-driven deep le
Externí odkaz:
http://arxiv.org/abs/2311.07166
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 MDE as a classification-regression problem where a linear combination of
Externí odkaz:
http://arxiv.org/abs/2309.14137
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 learning framework for monocular depth estimation by assuming that 3D
Externí odkaz:
http://arxiv.org/abs/2309.10592
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 current monocular vision methods are difficult to model stairs accurately w
Externí odkaz:
http://arxiv.org/abs/2308.06715
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 is critical for accurate depth estimation. Towards this end, we intro
Externí odkaz:
http://arxiv.org/abs/2302.08149
Stairs are common building structures in urban environments, and stair detection is an important part of environment perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sen
Externí odkaz:
http://arxiv.org/abs/2212.01098
SMUDLP: Self-Teaching Multi-Frame Unsupervised Endoscopic Depth Estimation with Learnable Patchmatch
Unsupervised monocular trained depth estimation models make use of adjacent frames as a supervisory signal during the training phase. However, temporally correlated frames are also available at inference time for many clinical applications, e.g., sur
Externí odkaz:
http://arxiv.org/abs/2205.15034
Autor:
Shen, Cheng, Pei, Zhongcai, Chen, Weihai, Zhou, Yi, Wang, Jianhua, Wu, Xingming, Chen, Jianer
Publikováno v:
In Engineering Applications of Artificial Intelligence October 2024 136 Part A
