{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:46:52Z","timestamp":1792039612195,"version":"4.4.0"},"reference-count":49,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202148"],"award-info":[{"award-number":["62202148"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62202148"],"award-info":[{"award-number":["62202148"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hubei Province","award":["2022CFB908"],"award-info":[{"award-number":["2022CFB908"]}]},{"name":"Natural Science Foundation of Hubei Province","award":["2019CFB530"],"award-info":[{"award-number":["2019CFB530"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201808420418"],"award-info":[{"award-number":["201808420418"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"award":["201808420418"],"award-info":[{"award-number":["201808420418"]}],"id":[{"id":"https:\/\/ror.org\/04atp4p48","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sparse radar point clouds provide limited evidence for three-dimensional (3D) object detection, making weak proposals especially sensitive to ranking and suppression. Standard post-processing depends on the primary detector\u2019s scores and geometry and can therefore remove useful candidates. We introduce an expert-consistency-guided refinement framework that acts on detector outputs without changing the backbone. Candidate boxes are retained and reweighted according to agreement with an independently trained radar expert; supported boxes are then adjusted by local geometric voting. A separate high-performance route instead trains a localization-quality-aware confidence head with relaxed positive assignment and does not use the expert-consistency gate. The framework is evaluated on Astyx, MAN TruckScenes, V2X-Radar-V, and K-Radar with three training seeds. At a 3D intersection-over-union threshold of 0.50, the strict route improves average precision (AP) in all 12 matched dataset\u2013seed comparisons, with a mean gain of 0.9726 AP. The high-performance route, whose gain comes from this quality-aware ranking branch rather than from expert-consistency gating, raises macro AP from 35.3552 to 39.1214, a gain of 3.7662 AP. These improvements are observations under the evaluated protocol: the strict voting parameters were selected using the same dataset\u2013seed cells, and leave-one-dataset-out threshold selection yields 11\/12 positive held-out differences. The strict route also shows metric-dependent confidence\u2013calibration trade-offs and requires an additional expert detector and voting computation. The results therefore support protocol-specific gains, not a guarantee of improved reliability on unseen data.<\/jats:p>","DOI":"10.3390\/s26196339","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:43:38Z","timestamp":1791564218000},"page":"6339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Expert-Consistency-Guided Proposal Refinement for 3D Object Detection from Sparse Radar Point Clouds"],"prefix":"10.3390","volume":"26","author":[{"given":"Zixuan","family":"Liu","sequence":"first","affiliation":[{"name":"Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3322-8955","authenticated-orcid":false,"given":"Wei","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China"},{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.52202\/068431-0276","article-title":"K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions","volume":"35","author":"Paek","year":"2022","journal-title":"Adv. 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