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  1. Home
  3. Multi-dimensional Quality Assessment For Single-image-to-3d Contents: Dataset And Model.
  1. Home
  3. Multi-dimensional Quality Assessment For Single-image-to-3d Contents: Dataset And Model.

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Related Experiment Videos

Multi-Dimensional Quality Assessment for Single-Image-to-3D Contents: Dataset and Model.

Kang Fu, Huiyu Duan, Zicheng Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Researchers developed the first subjective database (AIGC-SI23DCQA) for evaluating AI-generated 3D content from single images. They also proposed I3DQA, a novel objective quality assessment method, outperforming existing approaches.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Multimedia Processing

    Background:

    • AI-generated multimedia content, including 3D models, is rapidly advancing.
    • Quality evaluation for 2D AI content is established, but assessing single-image-to-3D content quality is underexplored.

    Purpose of the Study:

    • To establish the first comprehensive subjective evaluation database for single-image-to-3D content quality.
    • To benchmark existing quality assessment methods for this task.
    • To propose a novel objective quality assessment method for single-image-to-3D content.

    Main Methods:

    • Created AIGC-SI23DCQA database with 100 realistic, 100 AI-generated, and 100 CG input images.
    • Generated 1,500 3D contents using five algorithms and collected 94,500 annotations on texture fidelity, shape accuracy, and overall quality.
    • Developed I3DQA, an objective method using source image features, projected video, patches, and LMM features integrated via symmetric transformer blocks.

    Main Results:

    • Existing quality assessment methods show limitations for single-image-to-3D content.
    • The proposed I3DQA method demonstrates superior performance in objective quality assessment.
    • Experiments validate the effectiveness of I3DQA's components and its overall efficacy.

    Conclusions:

    • The AIGC-SI23DCQA database provides a foundational resource for 3D content quality assessment research.
    • The I3DQA method offers a robust framework for effective single-image-to-3D content quality evaluation.
    • This work advances the field of AI-generated 3D content quality assessment.