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Express"],"published-print":{"date-parts":[[2026,10,5]]},"abstract":"<jats:p>Structured light (SL)-based three-dimensional (3-D) reconstruction has been extensively utilized across various domains, including artificial intelligence, surveying, and precision manufacturing. In recent years, metasurface-facilitated structured light microsystems exhibit better performance in FOV, high resolution,and ultracompact volume than traditional optical projectors. However, the reconstructed point cloud data is always sparse and low-accuracy due to the limited number of projected dots depend on the fabrication size of the metasurface. In this work, we proposed a physics-aware depth estimation network for accurate depth and disparity estimation. To build a high-quality meta-optics structured light virtual dataset for training the network, our approach proposes a forward imaging process that integrates accurate meta-diffractive propagation modeling. Specifically, we achieve an absolute depth error of 0.173\u2005mm and improve the depth-estimation accuracy by 42.56% in standard deviation compared with the common SGBM algorithm, achieving high-accuracy 3-D measurement for the recovery of the fine details of complex surfaces. Such a physics-aware depth estimation network promises accurate, fast, and dense 3-D imaging results with a compact metasurface SL module.<\/jats:p>","DOI":"10.1364\/oe.606203","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T07:00:07Z","timestamp":1789023607000},"page":"36797","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Physics-aware depth estimation network for dense, accurate, and fast metasurface-based structured light microsystem"],"prefix":"10.1364","volume":"34","author":[{"given":"Chi","family":"Sun","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xnqep60","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Jing","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xnqep60","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jidong","family":"Xue","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xnqep60","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongshan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2668-8221","authenticated-orcid":true,"given":"Rui","family":"You","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xnqep60","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"oe-34-20-36797-R1","doi-asserted-by":"publisher","first-page":"5925","DOI":"10.1364\/AO.430101","type":"journal-article","volume":"60","author":"Zhou","year":"2021","journal-title":"Appl. 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