From a single picture of a scene, people can typically grasp the spatial layout immediately and even make good guesses at materials properties and where light is coming from to illuminate the scene. For example, we can reliably tell which objects occlude others, what an object is made of and its rough shape, regions that are illuminated or in shadow, and so on. It is interesting how little is known about our ability to make these determinations; as such, we are still not able to robustly "teach" computers to make the same high-level observations as people. This document presents algorithms for understanding intrinsic scene properties from single images. The goal of these inverse rendering techniques is to estimate the configurations of scene elements (geometry, materials, luminaires, camera parameters, etc) using only information visible in an image. Such algorithms have applications in robotics and computer graphics. One such application is in physically grounded image editing: photo editing made easier by leveraging knowledge of the physical space. These applications allow sophisticated editing operations to be performed in a matter of seconds, enabling seamless addition, removal, or relocation of objects in images.

中文翻译：

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物理接地图像编辑的逆向渲染技术

从场景的单张图片中，人们通常可以立即掌握空间布局，甚至可以很好地猜测材料属性以及光线从哪里来照亮场景。例如，我们可以可靠地判断哪些物体遮挡了其他物体、物体是由什么构成的及其粗略的形状、被照亮或处于阴影中的区域等等。有趣的是，人们对我们做出这些决定的能力知之甚少。因此，我们仍然无法有力地“教”计算机进行与人类相同的高级观察。本文档介绍了从单个图像中理解内在场景属性的算法。这些逆向渲染技术的目标是估计场景元素的配置（几何、材料、灯具、相机参数、等）仅使用图像中可见的信息。这种算法在机器人学和计算机图形学中有应用。其中一个应用是基于物理的图像编辑：利用物理空间的知识使照片编辑变得更容易。这些应用程序允许在几秒钟内执行复杂的编辑操作，从而实现图像中对象的无缝添加、删除或重新定位。