Anthropometric clothing measurements from 3D body scans

TL;DR

This paper presents a comprehensive pipeline for extracting accurate anthropometric measurements from 3D body scans, combining point cloud processing, model fitting, and regression analysis.

Contribution

It introduces a novel pipeline integrating 3D scanning, model fitting with non-rigid ICP, and regression for precise anthropometric measurements.

Findings

Mean absolute errors ranged from 2.5 mm to 16.0 mm.

Successfully scanned 375 subjects with high accuracy.

Pipeline is effective for both male and female body measurements.

Abstract

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid Iterative Closest Point (ICP) algorithm that minimizes overall energy of point distance and local stiffness energy terms. In the third stage, we measure multiple circumference paths on the fitted model surface and use a non-linear regressor to provide the final estimates of anthropometric measurements. We scanned 194 male and 181 female subjects and the proposed pipeline provides mean absolute errors from 2.5 mm to 16.0 mm depending on the anthropometric measurement.