{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:48:41Z","timestamp":1768204121944,"version":"3.49.0"},"reference-count":55,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703287"],"award-info":[{"award-number":["61703287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2024\u2010MS\u2010137"],"award-info":[{"award-number":["2024\u2010MS\u2010137"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study presents DFSplat, a feed\u2010forward 3D Gaussian Splatting model utilizing depth feature fusion for the high\u2010quality reconstruction of a 3D scene from sparse multiview data and the production of novel view images. DFSplat enhances the robustness of depth prediction and the quality of geometric reconstruction by incorporating a pre\u2010trained monocular depth estimation module into the multiview feature matching branch, thereby addressing the limitations of current methods for multiview depth estimation in complex scenes. The method employs a content\u2010guided attention (CGA) module to adaptively integrate monocular depth features with multiview cost\u2010volume features, addressing the fusion difficulty arising from the disparity in encoding between low\u2010level and high\u2010level features. Experiments conducted on the extensive RealEstate10K and ACID data sets demonstrate that DFSplat surpasses current methodologies in PSNR, SSIM, and LPIPS measures, attaining state\u2010of\u2010the\u2010art performance. The innovation integrates the global consistency of monocular depth estimation with the local precision of multiview matching, optimizing 3D Gaussian parameters prediction through an efficient fusion strategy, thereby offering a novel approach for high\u2010quality scene reconstruction in sparse view scenarios.<\/jats:p>","DOI":"10.1002\/cpe.70521","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T04:32:42Z","timestamp":1766982762000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DFSplat: High\u2010Quality 3D Gaussian Splatting From Sparse Multi\u2010View Images Based on Feature Fusion"],"prefix":"10.1002","volume":"38","author":[{"given":"Qian","family":"Gao","sequence":"first","affiliation":[{"name":"Shenyang Aerospace University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4235-5972","authenticated-orcid":false,"given":"Bin","family":"Xu","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanyun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3678-5922","authenticated-orcid":false,"given":"Linlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Aerospace University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"AVIC Civil Aircraft Airborne System Engineering Center Co. 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