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Shichao Fu

dblp:200/0818 · DBLP profile ↗
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2ranked-venue papers
2as first author
1since 2021 · last 2025
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › correspondence estimation › dense correspondence
dense correspondence fields
0.312017
Optimization of Camera Arrangement Using Correspondence Field to Improve Depth Estimation · IEEE Trans. Image Process. 2017
Computer vision › 3D vision
depth estimation
0.312017
Optimization of Camera Arrangement Using Correspondence Field to Improve Depth Estimation · IEEE Trans. Image Process. 2017
Computer vision › 3D vision › stereo vision
stereo matching
0.312017
Optimization of Camera Arrangement Using Correspondence Field to Improve Depth Estimation · IEEE Trans. Image Process. 2017

Methods — techniques the papers use, named apart from their topics

gradient-based optimization · 0.3
YearPublicationVenuePosition
2025 GEETI: Graph Embedding-Enhanced Textual Inversion for Chinese Harmful Meme Detection and Identification
Shichao Fu, Tongguan Wang, Ying Sha
ICIC (10)1
2017 Optimization of Camera Arrangement Using Correspondence Field to Improve Depth Estimation
abstract
Stereo matching algorithms attempt to estimate depth from the images obtained by two cameras. In most cases, the arrangement of cameras (their locations and orientations with respect to the scene) is determined based on human experience. In this paper, it is shown that the camera arrangement can be optimized using the concept of correspondence field (CF) for better acquisition of depth. Specifically, this paper demonstrates the relationship between the CF of a pair of cameras and depth estimation accuracy and presents a method to optimize their arrangement based on the gradient of the CF. The experimental results show that a pair of cameras optimized by the proposed method can improve the accuracy of depth estimation by as much as 30% compared with the conventional camera arrangements.
Shichao Fu, Farzad Safaei, Wanqing Li 0001
IEEE Trans. Image Process.1