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With the availability of low-cost and compact 2.5/3D visual sensing devices,computer vision community is experiencing a growing interest in visual sceneunderstanding of indoor environments. This survey paper provides acomprehensive background to this research topic. We begin with a historicalperspective, followed by popular 3D data representations and a comparativeanalysis of available datasets. Before delving into the application specificdetails, this survey provides a succinct introduction to the core technologiesthat are the underlying methods extensively used in the literature. Afterwards,we review the developed techniques according to a taxonomy based on the sceneunderstanding tasks. This covers holistic indoor scene understanding as well assubtasks such as scene classification, object detection, pose estimation,semantic segmentation, 3D reconstruction, saliency detection, physics-basedreasoning and affordance prediction. Later on, we summarize the performancemetrics used for evaluation in different tasks and a quantitative comparisonamong the recent state-of-the-art techniques. We conclude this review with thecurrent challenges and an outlook towards the open research problems requiringfurther investigation.

Indoor Scene Understanding in 2.5/3D for Autonomous Agents: A Survey