The retrieval of the 3D pose and shape of objects from images is an ill-posed
problem. A common way to object reconstruction is to match entities such as
keypoints, edges, or contours of a deformable 3D model, used as shape prior, to
their corresponding entities inferred from the image. However, such approaches
are highly sensitive to model initialisation, i
本文提出了一种可扩展，高效和准确的方法，用于检索野外对象的 3D 模型，包括了 3D 姿态估计，使用姿态先验来检索 3D 模型，使用基于 CNN 的多视图度量学习方法从 RGB 图像中检索图像描述符与采用的渲染深度图像匹配的深度图像得出精准的 3D 模型，报告了 Pascal3D + 上 3D 模型的定量结果。