  Many research works focus on leveraging the complementary geometric
information of indoor depth sensors in vision tasks performed by deep
convolutional neural networks, notably semantic segmentation. These works deal
with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The
challenges and resulting solutions of this task differ from its standard RGB
counterpart. This results in a new active research topic. The objective of this
paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D
Indoor Semantic Segmentation. This review presents the most popular public
datasets, proposes a categorization of the strategies employed by recent
contributions, evaluates the performance of the current state-of-the-art, and
discusses the remaining challenges and promising directions for future works.
