Semantic Segmentation of Indoor Scenes Based on RGBD images Feature Fusion

Abstract

Depth learning has been applied in semantic segmentation and object recognition in computer vision. In this paper, we propose a high-efficiency pixel classification convolutional neural network based on encoder-decoder structure. Using depth image to enhance CNNS network make color image and depth information can be detected jointly. The experimental data shows that through the fusion of depth features representing different scales of information, the algorithm architecture can be jointly optimized and obtain more sophisticated image semantic features. Compared with similar methods, image semantic segmentation algorithm has obvious advantages.