# Automated Single-Sensor 3D Scanning and Modular Benchmark Objects for Human-Scale 3D Reconstruction

## TL;DR

This paper introduces a single-sensor 3D scanning system that can accurately capture human-sized objects using a controlled setup, avoiding the need for expensive multi-sensor systems.

## Contribution

A marker-less, single-sensor 3D scanning platform with a modular benchmark object for human-scale reconstruction is proposed.

## Key findings

- The system achieves a mean surface density of 0.760 points/mm² on front-facing surfaces.
- Geometric deviation analysis shows a mean signed error of −1.54 mm with a volumetric error of 0.096%.
- The system mitigates drift and bending artifacts common in handheld scanning.

## Abstract

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 “telescoping”) 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 −1.54 mm (σ= 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.