Many existing approaches for image-to-geometry registration assume that either a textured 3D model or a good initial guess of the 3D pose is available to bootstrap the registration process. In this paper we consider the registration of photographs to 3D models even when no texture information is available. This is very challenging as we cannot rely on texture gradients, and even shading gradients are hard to estimate since the lighting conditions are unknown. To that end, we propose average shading gradients, a rendering technique that estimates the average gradient magnitude over all lighting directions under Lambertian shading. We use this gradient representation as the building block of a registration pipeline based on matching sparse features. To cope with inevitable false matches due to the missing texture information and to increase robustness, the pose of the 3D model is estimated in two stages. Coarse pose hypotheses are first obtained from a single correct match each, subsequently refined using SIFT flow, and finally verified. We apply our algorithm to registering images of real-world objects to untextured 3D meshes of limited accuracy. Moreover, we show that registration can be performed even for paintings despite lacking photo-realism.

中文翻译：

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使用平均着色梯度将图像自动配准到无纹理几何体

许多现有的图像到几何配准方法假设纹理 3D 模型或 3D 姿势的良好初始猜测可用于引导配准过程。在本文中，我们考虑将照片配准到 3D 模型，即使没有可用的纹理信息。这是非常具有挑战性的，因为我们不能依赖纹理梯度，而且由于光照条件未知，甚至着色梯度也很难估计。为此，我们提出了平均着色梯度，这是一种渲染技术，可以估计朗伯着色下所有光照方向的平均梯度大小。我们使用这种梯度表示作为基于匹配稀疏特征的注册管道的构建块。为了应对由于缺少纹理信息而导致的不可避免的错误匹配并提高鲁棒性，3D 模型的姿态估计分两个阶段进行。粗略的姿势假设首先从每个正确的匹配中获得，随后使用 SIFT 流进行细化，最后进行验证。我们将我们的算法应用于将真实世界对象的图像配准到精度有限的无纹理 3D 网格。此外，我们表明尽管缺乏照片真实感，但即使是绘画也可以进行配准。