RGB-D Semantic Segmentation for Indoor Modeling Using Deep Learning: A Review

With the availability and low cost of RGB-D sensors, indoor 3D modeling from RGB-D data has gained more interest in the research community. However, this topic is still challenging because of the complexity of indoor environments and the poor quality of RGB-D data. To deal with this problem, a focus on semantic segmentation as a first and crucial step in 3D modeling process is primordial. The main purpose of this paper is to offer a review of recent researches carried out on RGB-D semantic segmentation. Especially approaches based on deep neural network, their datasets, their metrics, and their challenges and limits are presented. Based on this state of the art, guidelines to improve research in this field are proposed.