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Updated: Jan 24, 2026

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Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors.

Yao Lu1, Shang Zhao1, Naji Younes2

  • 1Department of Computer Science, School of Engineering and Applied Science, Institute for Computer Graphics, The George Washington University, 800 22nd Street NW Suite 3400, Washington, DC 20052, USA.

Computer Animation and Virtual Worlds
|June 4, 2019
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Summary

This study introduces a cost-effective 3D body reconstruction system using consumer depth sensors for accurate medical applications. The developed system offers reliable 3D body shapes, making advanced scanning accessible to professionals and the public.

Keywords:
body composition inferencemedical applicationmultimodality registrationnonrigid registrationskeletal joints inferencesupervised learningsurface reconstruction system

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • 3D modeling technology has advanced significantly, but high-end systems are prohibitively expensive, and consumer-level devices lack medical-grade accuracy.
  • Existing consumer-grade 3D scanners are unsuitable for precise medical applications due to limitations in accuracy and reliability.

Purpose of the Study:

  • To develop a cost-effective and user-friendly 3D body reconstruction system utilizing consumer-grade depth sensors.
  • To achieve high accuracy and reliability in reconstructed body shapes for medical applications.
  • To enable wider accessibility of accurate 3D body scanning technology.

Main Methods:

  • A surface registration framework integrating articulated motion assumption, global loop closure constraint, and an as-rigid-as-possible deformation model.
  • A novel approach for accurate skeletal joint inference from anatomical data using multimodality registration.
  • A supervised predictive model for inferring skeletal joints independent of anatomical data reference.

Main Results:

  • The system successfully reconstructs 3D body shapes with accuracy and reliability suitable for medical applications.
  • Rigorous validation tests on real subjects confirmed the system's reconstruction accuracy and repeatability.
  • The proposed methods enhance reconstruction quality and enable skeletal joint inference.

Conclusions:

  • The developed system offers a cost-effective solution for accurate 3D body surface scanning.
  • This technology has the potential to make advanced body scanning accessible to medical professionals and the public.
  • The system can facilitate the derivation of health data, such as body fat percentage, from 3D body shapes.