Depth extraction from a single image by sampling based on distance metric learning

In this paper, we propose a new depth sampling method based on a learned Mahalanobis distance (DSMD), instead of traditional Euclidean distance (DSED), for depth extraction from a single image. This metric is learned with the goal that images with similar and dissimilar 3D structures to the query image are separated by a large margin. Thus the learned distance metric can better measure the similarity of 3D structure between images than Euclidean distance. We also propose a simple method based on Gaussian weighting function (D-FGW) for depth fusion of the sampled images. Experiments show that our DSMD method produces more accurate depth estimation of the query image than the DSED method does. Our DFGW method is fast and produces decent results. With the depth fusion method based on energy function minimization [1], our DSMD method achieves state-of-the-art results on Make 3D dataset.