Video object segmentation, i.e., the separation of a target object from
background in video, has made significant progress on real and challenging
videos in recent years. To leverage this progress in 3D applications, this
paper addresses the problem of learning to estimate the depth of segmented
objects given some measurement of camera motion (e.g., from robot kinematics or
vehicle odometry). We achieve this by, first, introducing a diverse, extensible
dataset and, second, designing a novel deep network that estimates the depth of
objects using only segmentation masks and uncalibrated camera movement. Our
data-generation framework creates artificial object segmentations that are
scaled for changes in distance between the camera and object, and our network
learns to estimate object depth even with segmentation errors. We demonstrate
our approach across domains using a robot camera to locate objects from the YCB
dataset and a vehicle camera to locate obstacles while driving.

本论文致力于解决如何从摄像机测量的数据中，使用视频目标分割技术学习估计物体深度的问题，为此我们首先引入了一个多样化的，可扩展的数据集，其次设计了一种新颖的深度学习网络，该网络仅使用分割掩模和不合格的摄像机运动即可估计物体深度，我们的方法可以通过机器人和车载摄像头，在不同领域进行定位目标和避开障碍物的实验。