论文标题
野外的单视图
Single View Metrology in the Wild
论文作者
论文摘要
大多数3D重建方法只能将场景属性恢复到全球规模的歧义。我们提出了一种新颖的方法来实现单个视图计量学，该方法可以恢复场景的绝对尺度，该场景的绝对尺度由地面上方的物体或摄像头高度的3D高度以及方向和视场的摄像机参数，仅使用在无约束条件下获得的单眼图像。我们的方法依赖于一个由深层网络学到的数据驱动的先验，专门设计，该网络通过估计边界框投影的估计，从未知相机与3D实体（例如对象高）等3D实体的相互作用中相互作用。我们利用自然图像中通常出现的人类或汽车等物体的分类先验，作为规模估计的参考。我们在几个数据集以及包括虚拟对象插入在内的应用程序上展示了最新的定性和定量结果。此外，我们的产出的感知质量通过用户研究验证。
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground as well as camera parameters of orientation and field of view, using just a monocular image acquired in unconstrained condition. Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights, through estimation of bounding box projections. We leverage categorical priors for objects such as humans or cars that commonly occur in natural images, as references for scale estimation. We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion. Furthermore, the perceptual quality of our outputs is validated by a user study.
