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Three-Dimensional Force System01:30

Three-Dimensional Force System

In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...

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Related Experiment Video

Updated: Jun 11, 2026

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone
09:26

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Published on: January 2, 2020

Person-in-WiFi 3D: Unified Model for 3D WiFi Perception.

Bo Qian, Xing Wei, Kangwei Yan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 9, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Person-in-WiFi 3D uses multiple WiFi devices for private, accurate multi-person 3D human perception. This system achieves breakthroughs in 3D pose and mesh estimation, outperforming previous WiFi sensing methods.

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    Area of Science:

    • Computer Vision
    • Wireless Sensing
    • Human-Computer Interaction

    Background:

    • WiFi sensing offers privacy and occlusion resilience, advancing applications in smart homes and elderly care.
    • Previous WiFi systems primarily focused on single-person 2D or 3D pose estimation.
    • Significant progress has been made in human pose and mesh estimation using various sensing modalities.

    Purpose of the Study:

    • To introduce Person-in-WiFi 3D, a novel WiFi system for multi-person 3D human perception.
    • To overcome limitations of single-transmitter/receiver WiFi systems for complex 3D scenarios.
    • To enable accurate and efficient 3D pose and mesh reconstruction of multiple individuals using WiFi signals.

    Main Methods:

    • Employed a multi-device WiFi system for enhanced 3D spatial reflection capture.
    • Utilized a DETR-like architecture with Hungarian matching for end-to-end estimation.
    • Implemented a hierarchical refinement strategy (global, instance, keypoint levels) for fine-grained reconstruction.

    Main Results:

    • Achieved a keypoint localization error of 93mm for 3D pose estimation.
    • Attained a mesh reconstruction error of 41mm.
    • Developed and validated the system on the Wiception3D dataset, comprising over 97K frames.

    Conclusions:

    • Person-in-WiFi 3D demonstrates storage efficiency, accuracy, and speed compared to prior systems.
    • The system achieves performance comparable to camera and millimeter-wave radar systems.
    • This work represents a significant advancement in multi-person 3D human perception using WiFi sensing.