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Gauss's Law: Spherical Symmetry01:26

Gauss's Law: Spherical Symmetry

A charge distribution has spherical symmetry if the density of charge depends only on the distance from a point in space and not on the direction. In other words, if the system is rotated, it doesn't look different. For instance, if a sphere of radius R is uniformly charged with charge density ρ0, then the distribution has spherical symmetry. On the other hand, if a sphere of radius R is charged so that the top half of the sphere has a uniform charge density ρ1 and the bottom half has a uniform...
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Gauss's Law: Planar Symmetry01:27

Gauss's Law: Planar Symmetry

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Updated: May 22, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Anchor-Driven Compact Gaussian Splatting for Dynamic Scene Reconstruction.

He Huang, Qi Yang, Mufan Liu

    IEEE Transactions on Visualization and Computer Graphics
    |May 20, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Anchor-driven compact Gaussian splatting (ADC-GS++) enhances dynamic scene reconstruction by optimizing Gaussian primitives. This method significantly boosts rendering speed and reduces storage while maintaining high quality.

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    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy (DIHM) of Weakly-scattering Subjects

    Published on: February 8, 2014

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • 3D Reconstruction

    Background:

    • Existing 4D Gaussian Splatting methods suffer from inefficiency due to per-Gaussian deformation.
    • This overlooks redundancy in spatially and temporally adjacent Gaussian primitives.

    Purpose of the Study:

    • To propose ADC-GS++, an anchor-driven framework for efficient and high-quality dynamic scene reconstruction.
    • To improve rendering speed and reduce storage consumption in 4D Gaussian Splatting.

    Main Methods:

    • Organizes Gaussian primitives using an anchor-based canonical representation for attribute sharing.
    • Employs static-dynamic decomposition and coarse-to-fine deformation driven by dynamic anchors.
    • Utilizes unified rate-distortion optimization and temporal significance-based anchor refinement.

    Main Results:

    • Achieves rendering speed improvements of 300%-700% over deformation-based methods.
    • Maintains competitive rendering quality with significantly reduced storage consumption.
    • Demonstrates a more favorable rate-distortion trade-off across various bitrates.

    Conclusions:

    • ADC-GS++ offers a substantial advancement in efficient and high-quality dynamic scene reconstruction.
    • The anchor-driven approach effectively addresses limitations of previous Gaussian Splatting techniques.
    • This framework provides a robust solution for complex and large-scale dynamic scene modeling.