Signal Processing - May 2017 - 55
Zhiwei Xiong, Yueyi Zhang, Feng Wu, and Wenjun Zeng
Computational Depth Sensing
Toward high-performance commodity
depth cameras
D
IMAGES LICENSED BY INGRAM PUBLISHING
epth information plays an important role in a variety of applications, including
manufacturing, medical imaging, computer vision, graphics, and virtual/augmented reality (VR/AR). Depth sensing has thus attracted sustained attention
from both academia and industry communities for decades. Mainstream depth
cameras can be divided into three categories: stereo, time of flight (ToF), and structured light. Stereo cameras require no active illumination and can be used outdoors,
but they are fragile for homogeneous surfaces [1]. Recently, off-the-shelf light field
cameras have demonstrated improved depth estimation capability with a multiview
stereo configuration [2]. ToF cameras operate at a high frame rate and fit time-critical scenarios well, but they are susceptible to noise and limited to low resolution [3].
Structured light cameras can produce high-resolution, high-accuracy depth, provided that a number of patterns are sequentially used. Due to its promising
and reliable performance, the structured light approach has been widely
adopted for three-dimensional (3-D) scanning purposes. However,
achieving real-time depth with structured light either requires highspeed (and thus expensive) hardware or sacrifices depth resolution and
accuracy by using a single pattern instead [4].
The Microsoft Kinect, the world's first consumer-grade depth camera, has brought depth-sensing technology into a new era. Taking
the structured light approach, the Kinect uses a unique speckle
pattern that can be generated by a low-cost laser-diffuser emitter
and relies on parallel computation to acquire depth at 30 frames
per second (fps) with a resolution of 640 × 480 pixels [5], [6].
The Kinect leverages distinct optics design and powerful computing capabilities for a decent performance. This commodity
depth camera has introduced revolutionary changes in various
related research areas. Taking computer vision as an example,
many problems that were considered challenging before can now
be solved with this easily accessible depth [7]. On the other
hand, the large demand for depth data has imposed higher
requirements for depth-sensing technology. Real-time
depth with even higher resolution and accuracy
is desirable in many practical applications.
In the future, more imaging modalities,
such as those deployed in mobile
devices, game consoles, robots,
drones, and emerging VR/AR
Digital Object Identifier 10.1109/MSP.2017.2669347
Date of publication: 26 April 2017
1053-5888/17©2017IEEE
1053-5888/17©2017IEEE
IEEE SIgnal ProcESSIng MagazInE
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May 2017
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Table of Contents for the Digital Edition of Signal Processing - May 2017
Signal Processing - May 2017 - Cover1
Signal Processing - May 2017 - Cover2
Signal Processing - May 2017 - 1
Signal Processing - May 2017 - 2
Signal Processing - May 2017 - 3
Signal Processing - May 2017 - 4
Signal Processing - May 2017 - 5
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Signal Processing - May 2017 - Cover3
Signal Processing - May 2017 - Cover4
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