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为可见和不可见的类别进行单视图3D网格重建

Xianghui Yang, Guosheng Lin, Luping Zhou

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|May 30, 2023

概括

这项研究介绍了GenMesh,这是一种用于单视图3D网格重建的新方法,可以概括到未见对象类别. 通过对重建过程进行因子化和增强几何特征学习,GenMesh 改进了从图像中恢复 3D 形状.

科学领域:

计算机视觉计算机视觉
3D 计算机图形 3D 计算机图形
机器学习机器学习

背景情况:

从RGB图像中重建单视图3D对象是一个具有挑战性的计算机视觉问题.
现有的深度学习方法难以将其推广到在培训过程中未见过的新型对象类别.

研究的目的:

开发一种用于单视图3D网格重建的方法,该方法可以有效地对未见对象类别进行概括.
鼓励模型更准确,更真实地重建3D对象,不管它们的训练数据如何.
在3D重建中分解特定类别的限制.

主要方法:

提出了GenMesh,一个端到端的两级网络,用于单视图3D网格重建.
将图像到网格映射分为图像到点和点到网格映射,后者不那么依赖类别.
采用2D和3D空间的本地特征采样策略,以捕获共享的几何性质并增强概括性.
引入了表面生成的多视图轮损失,补充了传统的点对点监控.

主要成果:

在ShapeNet和Pix3D数据集上,GenMesh显著优于现有的方法.
在各种指标和场景中表现出卓越的性能,特别是在重建新型对象类别时.
拟议的方法显示了对看不见的对象增强的概括能力.

结论:

拟议的GenMesh网络有效地解决了单视图3D网格重建中的泛化挑战.
阶段因子化,局部特征采样和多视图轮损失的结合使得新类别的重建准确性得到改善.
这项工作通过使单个图像的3D形状恢复更加强大和多功能,从而推动了该领域的发展.

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