学习恢复压缩点云属性:完全基于数据的方法和基于规则的解卷优化

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Updated: Jul 1, 2025

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

455

Junteng Zhang, Junzhe Zhang, Dandan Ding

    IEEE transactions on visualization and computer graphics
    |March 12, 2024
    PubMed
    概括

    本研究比较了两种方法来恢复基于几何的点云压缩 (G-PCC) 的视觉质量. 神经SAO提供了显著的改进,但是复杂的,而NeuralBF更简单,更好地概括G-PCC属性恢复.

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