Yongqiang Shi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yongqiang Shi has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Gang Wu
School of Computer and Artificial Intelligence, ZhengZhou University, Zhengzhou, China
1 shared publication - Jin Wang
Beijing Institute of Artificial Intelligence, School of Computer Science, Beijing University of Technology, Beijing, China
1 shared publication - Xu Gao
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
1 shared publication - Yanan Wang
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
1 shared publication - Yaqian Zhao
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
1 shared publication
Latest Publications
- Open Access
- 14 January 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (2) , 769
CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
Solving the depth estimation problem in a 360° image space, which has holistic scene perception, has become a trend in recent years. However, depth estimation in common 360° images is prone to geometric distortion. Therefore, this study proposes a new method, CAPDepth, to address the geometric-distortion problem of 360° monocular depth estimation. We reduce the tangential projections by an optimized content-aware projection (CAP) and a geometric embedding module to capture more features for global depth consistency. Additionally, we adopt an index map and a de-blocking scheme to improve the inference efficiency and quality of our CAPDepth model. Our experiments show that CAPDepth greatly alleviates the distortion problem, producing smoother, more accurate predicted depth results, and improves performance in panoramic depth estimation.
- 14 January 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (2) , 769
Open AccessCAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
Solving the depth estimation problem in a 360° image space, which has holistic scene perception, has become a trend in recent years. However, depth estimation in common 360° images is prone to geometric distortion. Therefore, this study proposes a new method, CAPDepth, to address the geometric-distortion problem of 360° monocular depth estimation. We reduce the tangential projections by an optimized content-aware projection (CAP) and a geometric embedding module to capture more features for global depth consistency. Additionally, we adopt an index map and a de-blocking scheme to improve the inference efficiency and quality of our CAPDepth model. Our experiments show that CAPDepth greatly alleviates the distortion problem, producing smoother, more accurate predicted depth results, and improves performance in panoramic depth estimation.