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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
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Joints01:26

Joints

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
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Unsupervised Category-Specific Partial Point Set Registration via Joint Shape Completion and Registration.

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    This study introduces a novel self-supervised method for partial point set registration. By integrating shape completion, the approach enhances registration accuracy for incomplete 3D data without ground truth supervision.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Geometric Deep Learning

    Background:

    • Learning-based methods excel at full point set registration but struggle with partial shapes.
    • Existing approaches often require explicit feature embedding networks.
    • Performance degradation is a key challenge when registering incomplete 3D data.

    Purpose of the Study:

    • To develop a self-supervised method for accurate partial point set registration.
    • To bridge the performance gap between full and partial shape registration.
    • To improve 3D shape analysis using incomplete observations.

    Main Methods:

    • A novel self-supervised approach incorporating a shape completion network.
    • Utilizing a learnable latent code shared between completion and registration networks.
    • Joint optimization of shared latent codes and decoder networks.
    • Unsupervised training without ground truth data.

    Main Results:

    • The proposed method effectively registers partial point sets.
    • Joint optimization enables latent codes to capture full shape information.
    • Demonstrated superior performance on the ModelNet40 dataset.
    • Achieved accurate shape completion and registration via latent code optimization during inference.

    Conclusions:

    • The integrated shape completion and registration framework significantly improves partial point set registration.
    • Self-supervised learning with shared latent codes offers a robust solution for incomplete 3D data.
    • The method provides an effective unsupervised alternative for 3D shape analysis tasks.