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Related Experiment Video

Updated: Jun 13, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

3D Pose Estimation Using Virtual Projection Based on 3D Reconstructed Model.

Jung-Woo Kim1, Sol Lee2, Byung-Seo Park2

  • 1Department of Electronic Materials Engineering, Kwangwoon University, 20, Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of Korea.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
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This study refines 3D human pose estimation from volumetric data using multi-view reconstruction and 2D projection. The method ensures stable, geometrically consistent 3D poses, validated against motion capture systems.

Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Human Pose Estimation

Background:

  • Accurate 3D human pose estimation is crucial for various applications.
  • Existing methods often struggle with noise and geometric inconsistencies in reconstructed data.
  • Volumetric capture systems offer rich 3D information but require robust pose refinement techniques.

Purpose of the Study:

  • To develop and validate a novel method for estimating and refining 3D human pose from 3D point clouds or mesh models.
  • To improve the stability and geometric consistency of 3D pose estimation from volumetric data.
  • To achieve accurate 3D skeleton estimation by integrating multi-view reconstruction and 2D projection techniques.

Main Methods:

  • Reconstruction of a 3D model from multi-view cameras to obtain an accurate skeleton.
Keywords:
3D mesh3D point cloud3D skeletonjoint refinementmulti-view RGB-Dpose estimationvolumetric capture

Related Experiment Videos

Last Updated: Jun 13, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

  • Projection of the 3D model to four virtual planes to estimate four 2D skeletons with low error.
  • Refinement of candidate joints using vertex distribution, DBSCAN clustering, and sphere fitting within the body volume.
  • Combination and refinement of joints through back-projection and spatial distribution analysis of 3D information.

    • Main Results:

    • The proposed method successfully estimates stable and geometrically consistent 3D human poses from reconstructed volumetric data.
    • Quantitative evaluation using Mean Per Joint Position Error (MPJPE) demonstrated accuracy compared to ground truth.
    • Visual assessment and comparison with motion capture data confirmed the effectiveness of the 3D pose estimation.

    Conclusions:

    • The developed technique provides a robust approach for 3D human pose estimation from volumetric capture.
    • Integration of 3D reconstruction, 2D projection, and advanced refinement algorithms yields high-quality pose estimates.
    • This method advances the field of 3D pose estimation, particularly for applications utilizing volumetric data.