Recent years have seen the widespread diffusion of 3D sensors, mainly based on active technologies such as structured light and Time-of-Flight, enabling the development of very interesting 3D vision applications. This paper describes a compact 3D camera based on passive stereo vision technology suited for mobile/embedded vision applications. Our 3D camera is very compact, the overall area of the processing unit is smaller than a business card, lightweight, it weights less than 100 g including lenses, has a reduced power consumption, about 2 Watt processing stereo pairs at 30+ fps, and can be easily configured with different baselines and processing units according to specific application requirements. The overall design is mapped on a low cost FPGA, making the hardware design easily portable to other reconfigurable devices, and allows us to obtain in real-time accurate and dense depth maps according to state-of-the-art stereo vision algorithms.

A Compact 3D Camera Suited for Mobile and Embedded Vision Applications

MATTOCCIA, STEFANO;CASADIO, MARCO
2014

Abstract

Recent years have seen the widespread diffusion of 3D sensors, mainly based on active technologies such as structured light and Time-of-Flight, enabling the development of very interesting 3D vision applications. This paper describes a compact 3D camera based on passive stereo vision technology suited for mobile/embedded vision applications. Our 3D camera is very compact, the overall area of the processing unit is smaller than a business card, lightweight, it weights less than 100 g including lenses, has a reduced power consumption, about 2 Watt processing stereo pairs at 30+ fps, and can be easily configured with different baselines and processing units according to specific application requirements. The overall design is mapped on a low cost FPGA, making the hardware design easily portable to other reconfigurable devices, and allows us to obtain in real-time accurate and dense depth maps according to state-of-the-art stereo vision algorithms.
2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
195
196
S. Mattoccia; I. Marchio; M. Casadio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/413793
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