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  1. Home
  3. Interacted Planes Reveal 3d Line Mapping.
  1. Home
  3. Interacted Planes Reveal 3d Line Mapping.

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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Interacted Planes Reveal 3D Line Mapping.

Zeran Ke, Bin Tan, Gui-Song Xia

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 18, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    LiP-Map creates detailed 3D line maps from images by jointly optimizing line and planar primitives. This efficient method improves 3D reconstruction and visual localization in man-made environments.

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    Area of Science:

    • Computer Vision
    • Geometric Deep Learning
    • Robotics

    Background:

    • 3D line mapping from multi-view RGB images offers a structured scene representation.
    • Existing methods often lack explicit physical and topological scene understanding.
    • Man-made environments possess inherent geometric structures like planes and lines.

    Purpose of the Study:

    • To develop an efficient and accurate 3D line mapping framework.
    • To integrate physical and topological scene understanding into line mapping.
    • To improve structured reconstruction and line-assisted visual localization.

    Main Methods:

    • LiP-Map: A novel line-plane joint optimization framework.
    • Explicitly models learnable line and planar primitives.
  • Integrates planar topology via direct primitive interactions, not pairwise constraints.

    • Main Results:

    • Achieves accurate and detailed 3D line mapping with high efficiency (3-5 minutes per scene).
    • Demonstrates superior performance on diverse datasets (ScanNetV2, ScanNet++, Hypersim, 7Scenes, Tanks&Temple).
    • Significantly advances line-assisted visual localization accuracy.

    Conclusions:

    • LiP-Map provides a principled approach to structured 3D reconstruction in man-made environments.
    • The joint optimization of lines and planes enhances mapping quality and localization.
    • The framework offers a strong baseline for future research in geometric scene understanding.