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Wonder3D++:跨域扩散用于从单个图像中生成高保真度3D

Yuxiao Yang, Xiao-Xiao Long, Zhiyang Dou

IEEE transactions on pattern analysis and machine intelligence

|October 7, 2025

概括

Wonder3D++有效地从单个图像中创建高质量的3D纹理网格,使用一种新的跨域扩散模型. 这种方法克服了先前技术的局限性,为3D重建提供了更好的质量,一致性和速度.

科学领域:

计算机视觉计算机视觉
三维重建的3D重建
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

评分蒸采样 (SDS) 方法提供了从二维先验中恢复3D几何的潜力,但速度缓慢且不一致.
直接3D生成方法是快速的,但产生低质量的结果缺乏细节.

研究的目的:

开发一种高效,高保真的单视图3D重建方法.
解决现有的3D生成技术的局限性,专注于质量,一致性和速度.

主要方法:

介绍了Wonder3D++,一个跨域扩散模型,生成多视图正常地图和彩色图像.
采用多视图跨领域的注意力机制,以提高生成一致性.
开发了一种级联式3D网格提取算法,用于快速,高质量的表面生成.

主要成果:

从单视图图像中实现了高保真度纹理网格生成.
在各种输入中展示了强大的概括能力.
显示了显著的效率改进,在大约3分钟内进行网状提取.

结论:

Wonder3D++为单视图3D重建提供了一种卓越的方法,平衡质量,一致性和效率.
拟议的方法为从有限的输入数据中生成详细的3D模型设定了一个新的基准.

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