Depth-aware object instance segmentation
Linwei Ye, Zhi Liu, Yang Wang · 2017
We consider the problem of object instance segmentation. The goal is to label each pixel in an image according to its object class as well as its object instance. The proposed approach consists of three steps including object instance detection, category-specific instance segmentation and depth-aware ordering. The novelty of the proposed approach is that it uses the depth information to resolve the ambiguity of pixel labels when two object instances are overlapping. Experimental results on the PASCAL VOC 2012 benchmark demonstrate the competitive performance of the proposed approach compared with other state-of-the-art methods.