A Multi-Modal Deep Network for RGB-D Segmentation of Clothes
This publication appears in: Electronics Letters
Authors: B. Joseph Joukovsky, P. Hu and A. Munteanu
Publication Date: Jan. 2020
In this Letter, the authors propose a deep learning based method to perform semantic segmentation of clothes from RGB-D images of people. First, they present a synthetic dataset containing more than 50,000 RGB-D samples of characters in different clothing styles, featuring various poses and environments for a total of nine semantic classes. The proposed data generation pipeline allows for fast production of RGB, depth images and ground-truth label maps.Secondly, a novel multi-modal encoderecoder convolutional network is proposed which operates on RGB and depth modalities. Multi-modal features are merged using trained fusion modules which use multi-scale atrous convolutions in the fusion process. The method is numerically evaluated on synthetic data and visually assessed on real-world data. The experiments demonstrate the efficiency of the proposed model over existing methods.