{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:51:41Z","timestamp":1787237501041,"version":"build-2736575974"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as \u201ctelescoping\u201d) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of 0.760points\/mm2 on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of \u22121.54 mm (\u03c3= 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems.<\/jats:p>","DOI":"10.3390\/s26041331","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T11:44:27Z","timestamp":1771501467000},"page":"1331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Single-Sensor 3D Scanning and Modular Benchmark Objects for Human-Scale 3D Reconstruction"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4456-943X","authenticated-orcid":false,"given":"Kartik","family":"Choudhary","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering and Product Design Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3047-6811","authenticated-orcid":false,"given":"Mats","family":"Isaksson","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering and Product Design Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0315-645X","authenticated-orcid":false,"given":"Gavin W.","family":"Lambert","sequence":"additional","affiliation":[{"name":"Iverson Health Innovation Research Institute, Swinburne University of Technology, Hawthorn, VIC 3122, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8539-2960","authenticated-orcid":false,"given":"Tony","family":"Dicker","sequence":"additional","affiliation":[{"name":"Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Brisbane, QLD 4006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Balasubramanian, M., and Sheykhmaleki, P. 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