Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A 3D Point Cloud Gesture Estimation Method Based on EdgeConv Reconstruction of Joint Features. |
| Συγγραφείς: |
Yong J; The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.; School of Computer Science, Peking University, Beijing 100871, China., Lei X; College of Intelligence and Computing, Tianjin University, Tianjin 300072, China., Dang J; The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China., Zhang Z; The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China. |
| Πηγή: |
Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 Jul 14; Vol. 26 (14). Date of Electronic Publication: 2026 Jul 14. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: Basel, Switzerland : MDPI, c2000- |
| Ιατρικοί όροι (MeSH): |
Imaging, Three-Dimensional*/methods , Hand Joints*/physiology , Gestures*, Hand/physiology ; Humans ; Algorithms |
| Περίληψη: |
Gesture interaction allows users to interact with objects through natural hand movements without direct physical contact. However, in gesture interaction, the hand moves frequently with large variations in direction, and hand joints are prone to self-occlusion. The accuracy of existing 3D point cloud gesture estimation methods is insufficient to meet the requirements of natural gesture interaction. This article proposes a 3D point cloud gesture estimation method based on EdgeConv to reconstruct joint features. The method first converts depth maps into point cloud data to reduce the impact of viewpoint variations on depth map representation of the same gesture, thereby mitigating the effect on estimation accuracy. Then, the global features are reconstructed using EdgeConv in the initial joint estimation module so that the reconstructed features contain structural information between hand joints. Finally, local joint refinement is performed by constructing local features centered around the refined joint twice using EdgeConv. These features include reference information from within group points to group center points and structural information between joint points. Using EdgeConv to enrich hand joint feature information can effectively improve the performance of hand joint estimation. Comparative experimental analysis was conducted on the 3D point cloud datasets ICVL, NYU, and MSRA, and complex scene experimental analysis was conducted on the InterHand2.6M dataset. Furthermore, the development of virtual-real interaction applications was carried out. The experimental results show that the 3D point cloud gesture estimation method proposed in this paper has high accuracy, strong generalization and robustness, providing solid support for natural gesture interaction applications. |
| Contributed Indexing: |
Keywords: 3D point cloud; edgeconv; feature guidance; feature reconstruction; gesture estimation; inter-point relationship |
| Entry Date(s): |
Date Created: 20260728 Date Completed: 20260729 Latest Revision: 20260731 |
| Update Code: |
20260731 |
| PubMed Central ID: |
PMC13417416 |
| DOI: |
10.3390/s26144472 |
| PMID: |
42515355 |
| Βάση Δεδομένων: |
MEDLINE |