Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Ruiming Yu

dblp:402/2180 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0008-5419-4839ORCID · reported

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

Graphics, computer vision, multimedia, augmented reality and games · 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.

Computer graphics and multimedia
1 paper
Computational photography and imaging · 67% Image and video processing · 33%
Artificial intelligence
1 paper
3D vision · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational photography and imaging › 3d imaging
3d measurement
0.912025
NPC-SPU: Nonlinear Phase Coding-Based Stereo Phase Unwrapping for Efficient 3D Measurement · IEEE Trans. Image Process. 2025
Image and video processing › image restoration › inverse problem › signal reconstruction
phase unwrapping
0.912025
NPC-SPU: Nonlinear Phase Coding-Based Stereo Phase Unwrapping for Efficient 3D Measurement · IEEE Trans. Image Process. 2025
Computational photography and imaging › 3d scanning
structured light
0.912025
NPC-SPU: Nonlinear Phase Coding-Based Stereo Phase Unwrapping for Efficient 3D Measurement · IEEE Trans. Image Process. 2025
Computer vision › 3D vision
3d reconstruction
0.312025
NPC-SPU: Nonlinear Phase Coding-Based Stereo Phase Unwrapping for Efficient 3D Measurement · IEEE Trans. Image Process. 2025

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

nonlinear phase coding · 1.7local phase correlation matching · 1.7stereo constraints · 0.9stereo constraint · 0.9
YearPublicationVenuePosition
2025 NPC-SPU: Nonlinear Phase Coding-Based Stereo Phase Unwrapping for Efficient 3D Measurement
abstract
3D imaging based on phase-shifting structured light is widely used in industrial measurement due to its non-contact nature. However, it typically requires a large number of additional images (multi-frequency heterodyne (M-FH) method) or introduces intensity features that compromise accuracy (space domain modulation phase-shifting (SDM-PS) method) for phase unwrapping, and it remains sensitive to motion. To overcome these issues, this article proposes a nonlinear phase coding-based stereo phase unwrapping (NPC-SPU) method that requires no additional patterns while maintaining measurement accuracy. In the encoding stage, a novel nonlinear distortion feature is introduced, while the signal-to-noise ratio of the phase codeword is preserved. In the decoding stage, a local phase unwrapping method that does not require additional auxiliary information is first proposed, closely associating the distortion information in the local wrapped phase. Then, a pre-calibrated stereo constraint system is used to filter potential matching phases, significantly reducing phase ambiguity and computational costs. Finally, to avoid the time-consuming and complex intensity kernel matching used in traditional methods, we propose a local phase correlation matching (LPCM) technique that enables lightweight and robust phase unwrapping. Experimental results demonstrate that this algorithm significantly enhances 3D reconstruction performance in scenarios with large depth, large disparity, complex colored structures, and dynamic scenes. Specifically, in dynamic environments (20mm/s), the proposed method achieves a lower measurement error rate (0.7829% vs. 6.4962%) with only 3 patterns, compared to the traditional three-frequency heterodyne (T-FH) method (using 9 patterns). Additionally, its measurement accuracy outperforms the advanced SDM-PS method, which also uses 3 patterns (0.1102 mm vs. 0.3232 mm).
Ruiming Yu, Hongshan Yu, Wei Sun 0028, Yaonan Wang 0001, Naveed Akhtar, Kemao Qian
IEEE Trans. Image Process.1