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MVDiffusion++是一种多视图扩散模型，其结构包括一个用于单视图或稀疏视图输入图像的条件分支和一个用于输出图像的生成分支。 近期，一项名为MVDiffusion++的研究成果引起了业界的广泛关注，它以其独特的技术路径，为3D对象重建领域带来了一场革命性的变革。 1. 多视图图像的潜变量同时（即并行）通过共享的 UNet 进行去噪。 2. 将新的 对应感知注意 (CAA)块插入到UNet中，学习跨视图几何一致性，实现全局感知。 2.1 多视图潜在扩.

MVDiffusion++受人类视觉启发，能依据任意无位姿图片生成高分辨率有位姿图像以实现高质量3D重建。 该方法无需精确相机姿态，在新视角合成等多方面表现优异，超越现有. 本文提出了一种用于 3D 对象重建的神经架构 MVDiffusion++，在给定一张或几张没有相机姿势的图像的情况下，可以合成对象的密集且高分辨率的视图。

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