  With the availability of low-cost and compact 2.5/3D visual sensing devices,
computer vision community is experiencing a growing interest in visual scene
understanding of indoor environments. This survey paper provides a
comprehensive background to this research topic. We begin with a historical
perspective, followed by popular 3D data representations and a comparative
analysis of available datasets. Before delving into the application specific
details, this survey provides a succinct introduction to the core technologies
that are the underlying methods extensively used in the literature. Afterwards,
we review the developed techniques according to a taxonomy based on the scene
understanding tasks. This covers holistic indoor scene understanding as well as
subtasks such as scene classification, object detection, pose estimation,
semantic segmentation, 3D reconstruction, saliency detection, physics-based
reasoning and affordance prediction. Later on, we summarize the performance
metrics used for evaluation in different tasks and a quantitative comparison
among the recent state-of-the-art techniques. We conclude this review with the
current challenges and an outlook towards the open research problems requiring
further investigation.
