Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks

Sami Barchid (1) , José Mennesson (2, 1, 3) , Chaabane Chabane Djeraba (1)

1 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
2 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai
3 CERI SN - IMT Nord Europe - Centre for Digital Systems

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.

Mots clés: RGB-D Indoor Semantic Segmentation; Deep Convolutional Neural Networks; Deep Learning

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Sami Barchid, José Mennesson, Chaabane Chabane Djeraba. Review on Indoor RGB-D Semantic Segmentation with Deep Convolutional Neural Networks. CBMI 2021 - Content-based Multimedia Indexing, Jun 2021, Lille / Virtual, France. ⟨hal-03264035⟩