Consider the geo-localization task of finding the pose of a camera in a large 3D scene from a single image. Most existing CNN-based methods use as input textured images. We aim to experimentally explore whether texture and correlation between nearby images are necessary in a CNN-based solution for the geo-localization task. To do so, we consider lean images, textureless projections of a simple 3D model of a city. They only contain information related to the geometry of the scene viewed (edges, faces, and relative depth). The main contributions of this paper are: (i) to demonstrate the ability of CNNs to recover camera pose using lean images; and (ii) to provide insight into the role of geometry in the CNN learning process.

考虑地理定位任务，即从单个图像中查找大型3D场景中摄像机的姿势。现有的大多数基于CNN的方法都将其用作输入纹理图像。我们旨在通过实验探索在基于CNN的地理定位任务解决方案中是否需要纹理和附近图像之间的相关性。为此，我们考虑城市的简单3D模型的精益图像，无纹理投影。它们仅包含与所查看场景的几何形状（边缘，面和相对深度）有关的信息。本文的主要贡献是：（i）展示CNN使用瘦图像恢复相机姿态的能力；（ii）深入了解几何在CNN学习过程中的作用。