Size To Depth
Yiran Wu, Sihao Ying, Lianmin Zheng
We consider the problem of single monocular image depth estimation. It is a notoriously challenging problem due to its ill-posedness nature. Previous efforts can be roughly classified into two families: learning-based method and interactive method. The former, in which deep convolutional neural network (CNN) is adopted frequently, leads to considerable results on specific dataset, but perform poorly on images outside the dataset, which shows its lack of extensiveness. Besides, plenty of data are needed to train the model. The latter requires human annotation of depthwhich, however, is easily to have large errors. To overcome these problems, we propose a new perspective for single monocular image depth estimation problem: size to depth. Most previous interactive methods try to obtain depth labels directly from human. Different from these methods, our method receives object size labels from human as prior. Depth can be inferred through simple geometric relationships given size labels. Then we design a conditional random field (CRF) model to propagate depth information and finally generate the whole depth map. We experimentally demonstrate that our method outperforms traditional depth-labeling methods and can produce satisfactory depth maps.
Code & Paper
View code on Github.
View paper on Here.