Depth estimation in handheld augmented reality: a review

Abstract

Depth estimation involves capturing the depth information of a scene in the form of depth data. This depth information can be applied in computer vision tasks to enhance perception and comprehension. In handheld augmented reality (AR), depth estimation refers to the capability of a handheld device to estimate the depth or distance of objects in the real world based on input from its camera feed. Currently, there is a lack of work that reviews on this topic. Thus, this paper reviews and discusses the technologies regarding depth estimation on handheld devices and their applications in relation to AR. We employ partially the systematic review procedure to allow more specific focus for our, broken into three main focuses. First, we discuss the methods to obtain depth data on handheld devices. Next, we discuss on the existing frameworks that enable depth estimation for handheld AR. Then, we compile and discuss the applications of depth estimation for handheld AR based on the reviewed papers. Finally, we discuss the novelties and limitations of the current research to determine the gaps in this field of research.