对比三种弱标定方法，为手机端人体测量提供实证设计参考
An Empirical Study of Monocular Human Body Measurement Under Weak Calibration
- 基于关键点几何、姿态回归和物体轮廓的弱标定策略
- 标定越简便，测量稳定性越差，存在明显权衡关系
- 适合轻量级人体测量系统开发人员参考
从单目RGB图像中估计人体尺寸仍具挑战性，主要源于尺度模糊、视角敏感及缺乏显式深度信息。本文对三种弱标定单目策略进行了系统性实证研究：基于关键点的几何方法、姿态驱动的回归方法以及物体校准的轮廓方法，在使用消费级相机的半约束条件下进行评估。研究不追求最高精度，而是分析不同标定假设如何影响测量行为、鲁棒性及失效模式，涵盖多种体型。结果揭示了标定阶段用户投入与所得周向测量稳定性之间的明确权衡。本研究为面向消费设备部署的轻量化单目人体测量系统提供了实证设计依据。
原文摘要 · Abstract (English)
Estimating human body measurements from monocular RGB imagery remains challenging due to scale ambiguity, viewpoint sensitivity, and the absence of explicit depth information. This work presents a systematic empirical study of three weakly calibrated monocular strategies: landmark-based geometry, pose-driven regression, and object-calibrated silhouettes, evaluated under semi-constrained conditions using consumer-grade cameras. Rather than pursuing state-of-the-art accuracy, the study analyzes how differing calibration assumptions influence measurement behavior, robustness, and failure modes across varied body types. The results reveal a clear trade-off between user effort during calibration and the stability of resulting circumferential quantities. This paper serves as an empirical design reference for lightweight monocular human measurement systems intended for deployment on consumer devices.
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