{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T22:10:46Z","timestamp":1773094246919,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>The irregular and highly non-uniform spatial distribution inherent to dynamic three-dimensional (3D) point clouds (DPCs) severely hampers the extraction of reliable temporal context, rendering inter-frame compression a formidable challenge. Inspired by two-dimensional (2D) image and video compression methods, existing approaches attempt to model the temporal dependence of DPCs through a motion estimation\/motion compensation (ME\/MC) framework. However, these approaches represent only preliminary applications of this framework; point consistency between adjacent frames is insufficiently explored, and temporal correlation requires further investigation. To address this limitation, we propose a hierarchical ME\/MC framework that adaptively selects the granularity of the estimated motion field, thereby ensuring a fine-grained inter-frame prediction process. To further enhance motion estimation accuracy, we introduce a dual-attention-based KNN block-matching (DA-KBM) network. This network employs a bidirectional attention mechanism to more precisely measure the correlation between points, using closely correlated points to predict inter-frame motion vectors and thereby improve inter-frame prediction accuracy. Experimental results show that the proposed DPC compression method achieves a significant improvement (gain of 70%) in the BD-Rate metric on the 8iFVBv2 dataset. compared with the standardized Video-based Point Cloud Compression (V-PCC) v13 method, and a 16% gain over the state-of-the-art deep learning-based inter-mode method.<\/jats:p>","DOI":"10.3390\/jimaging11100332","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T12:05:13Z","timestamp":1758801913000},"page":"332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual-Attention-Based Block Matching for Dynamic Point Cloud Compression"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6006-5150","authenticated-orcid":false,"given":"Longhua","family":"Sun","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Qilu Normal University, No. 2 Wenbo Road, Jinan 250200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingrui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Qilu Normal University, No. 2 Wenbo Road, Jinan 250200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0918-3996","authenticated-orcid":false,"given":"Qing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Beijing University of Technology, No. 100 Pingleyuan, Beijing 100124, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Silva, A.L., Oliveira, P., Dur\u00e3es, D., Fernandes, D., N\u00e9voa, R., Monteiro, J., Melo-Pinto, P., Machado, J., and Novais, P. 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