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Related Experiment Video

Updated: Jul 10, 2026

How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds.

Ruikai Cui, Binzhu Xie, Shi Qiu

    IEEE Transactions on Visualization and Computer Graphics
    |July 8, 2026
    PubMed
    Summary
    This summary is machine-generated.

    NumGrad-Pull enhances 3D surface reconstruction by using tri-plane structures and numerical gradients for faster, more detailed results. This method improves the learning of signed distance functions (SDFs) from unoriented point clouds.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Geometric Modeling

    Background:

    • Reconstructing surfaces from 3D points is challenging.
    • Neural signed distance functions (SDFs) are used for surface reconstruction.
    • Existing methods face challenges with training stability and local detail fidelity.

    Purpose of the Study:

    • Introduce NumGrad-Pull, a novel framework for 3D surface reconstruction.
    • Leverage tri-plane structures to accelerate SDF learning and improve detail fidelity.
    • Address challenges in training stability, locality, and sparsity for unoriented point clouds.

    Main Methods:

    • Utilize tri-plane structures for efficient SDF representation.
    • Employ numerical gradients instead of analytical gradients for improved training stability.
    • Implement a progressive plane expansion strategy for faster convergence.
    • Develop a complementary data sampling strategy to reduce reconstruction artifacts.

    Main Results:

    • NumGrad-Pull demonstrates effectiveness and robustness across various benchmarks.
    • The approach achieves enhanced fidelity of local details in surface reconstruction.
    • Faster convergence and mitigation of reconstruction artifacts are observed.

    Conclusions:

    • The proposed NumGrad-Pull framework offers a synergistic approach to 3D surface reconstruction.
    • Numerical gradients, progressive expansion, and complementary sampling effectively address key challenges.
    • The method shows significant improvements in surface reconstruction quality and efficiency.