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Floor Plan Restoration: A Multimodal Method Under One Second.

Tao Wen, You-Ming Fu, Chun-Xia Xiao

    IEEE Transactions on Visualization and Computer Graphics
    |March 3, 2025
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    Summary

    MMParseNet accurately restores vector and semantic information from floor plan images using multimodal cues. This novel approach significantly improves accuracy and efficiency for floor plan restoration tasks.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Floor plan restoration is crucial for applications like interior design and layout planning.
    • Current two-stage methods (parsing and vectorization) lack accuracy and efficiency due to ignoring unique floor plan features.
    • Existing techniques struggle with the complexity of converting raster images to structured vector data.

    Purpose of the Study:

    • To develop an accurate and efficient method for floor plan restoration from raster images.
    • To leverage unique multimodal cues present in floor plans for improved parsing.
    • To optimize the vectorization process for faster restoration.

    Main Methods:

    • Proposed MMParseNet, incorporating multimodal cues like room names, furniture icons, and boundaries for accurate parsing.
    • Implemented an efficiency-optimized vectorization method using Principal Component Analysis (PCA).
    • Conducted experiments on multiple public and a self-built dataset for validation.

    Main Results:

    • MMParseNet demonstrated consistent improvements in parsing accuracy across diverse datasets.
    • The method achieved sub-second overall restoration times, significantly enhancing efficiency.
    • Qualitative and quantitative evaluations confirmed the effectiveness of the proposed approach.

    Conclusions:

    • MMParseNet effectively addresses the limitations of existing floor plan restoration methods.
    • The integration of multimodal cues and PCA-optimized vectorization leads to superior performance.
    • This research offers a promising solution for automated and efficient floor plan data recovery.