Monocular Depth Matching With Hybrid Sampling and Depth Label Propagation

This paper proposes a monocular depth label propagation model, which describes monocular images into depth label distribution for the target classification matching. 1) Depth label propagation by hybrid sampling and salient region sifting, improve the discrimination of detection feature categories. 2) Depth label mapping and spectrum clustering to classify target, define the depth of the sorting rules. The experimental results of motion recognition and 3D point cloud processing, show that this method can approximately reach the performance of all previous monocular depth estimation methods. The neural network model black box training learning module is not used, which improves the interpretability of the proposed model.