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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
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Related Experiment Video

Updated: Jul 9, 2025

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TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment.

Yujie Zhang, Qi Yang, Yifei Zhou

    IEEE Transactions on Visualization and Computer Graphics
    |December 1, 2023
    PubMed
    Summary

    This study introduces a new method for assessing point cloud quality by measuring transformation complexity. The transformational complexity based distortion metric (TCDM) achieves state-of-the-art results in perceptual quality evaluation.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Perceptual Quality Assessment

    Background:

    • Objective point cloud quality assessment (PCQA) aims for perceptually consistent quantitative metrics.
    • Existing methods often struggle to capture human visual system (HVS) perception accurately.

    Purpose of the Study:

    • To develop a novel objective metric for point cloud quality assessment.
    • To leverage cognitive science and HVS intuition for improved quality evaluation.

    Main Methods:

    • Point clouds are segmented into local patches using 3D Voronoi diagrams.
    • A space-aware vector autoregressive (SA-VAR) model encodes reference and distorted patches.
    • Transformational complexity is computed using covariance matrices and SA-VAR prediction terms.

    Main Results:

    • The proposed transformational complexity based distortion metric (TCDM) was evaluated on five public databases.
    • TCDM achieved state-of-the-art (SOTA) performance in point cloud quality assessment.
    • The metric demonstrated robustness across various experimental scenarios.

    Conclusions:

    • TCDM effectively measures point cloud quality by quantifying transformation complexity.
    • The approach offers a promising direction for perceptually accurate objective PCQA.
    • The method shows significant potential for real-world applications requiring high-fidelity point cloud data.