A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions

Gokturk, S.B.; Yalcin, Hakan; Bamji, Cyrus

doi:10.1109/cvpr.2004.291

Cited by 320 publications

(203 citation statements)

References 9 publications

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“…The reflected light is then compared to the original one, calculating the phase shift, by means of measuring the intensity of the incoming light since the phase shift is proportional to the time of flight of the light reflected by a distant object. A detailed description of the time-of-flight principle can be found in Gokturk et al (2004).…”

Section: Data Acquisitionmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D features extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

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Section: Data Acquisitionmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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“…Active depth cameras have been receiving a lot of attention since the recent introduction of Kinect from Microsoft. The sensor captures the distance information very reliably with a resolution of 640×480 and is available at a relatively low cost compared to that of other depth cameras [20], [21]. However the cost is still more than ten times that of general CMOS cameras, which is a critical factor in mass production.…”

Section: Proposed Algorithm 1 Obtaining 3d Informationmentioning

confidence: 99%

A Long-Range Touch Interface for Interaction with Smart TVs

Lee¹

2012

ETRI J

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A powerful interaction mechanism is one of the key elements for the success of smart TVs, which demand far more complex interactions than traditional TVs. This paper proposes a novel interface based on the famous touch interaction model but utilizes long-range bare hand tracking to emulate touch actions. To satisfy the essential requirements of high accuracy and immediate response, the proposed hand tracking algorithm adopts a fast colorbased tracker but with modifications to avoid the problems inherent to those algorithms. By using online modeling and motion information, the sensitivity to the environment can be greatly decreased. Furthermore, several ideas to solve the problems often encountered by users interacting with smart TVs are proposed, resulting in a very robust hand tracking algorithm that works superbly, even for users with sleeveless clothing. In addition, the proposed algorithm runs at a very high speed of 82.73 Hz. The proposed interface is confirmed to comfortably support most touch operations, such as clicks, swipes, and drags, at a distance of three meters, which makes the proposed interface a good candidate for interaction with smart TVs. Keywords: Smart TV, touch interface, bare hand tracking, human computer interaction. Manuscript received Oct. 19, 2011; revised Aug. 14, 2012; accepted Aug. 22, 2012. Jaeyeon Lee (phone: +82 42 860 5507, leejy@etri.re.kr), DoHyung Kim (dhkim008@etri.re.kr), Jaehong Kim (jhkim504@etri.re.kr ), Jae-il Cho (jicho@etri.re.kr), and Joochan Sohn (jcsohn@etri.re.kr) are with the IT Convergence Technology Research Laboratory, ETRI, Daejeon, Rep. of Korea.http://dx

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“…The chosen PMD CamCube 3.0 utilizes Radio Frequency (RF) modulated light sources with phase detectors. The modulated outgoing beam is sent out with a RF carrier, and the phase shift of that carrier is measured on the receiver side to compute the distance (Gokturk, et al, 2010). Compared with traditional LIDAR scanners and stereo vision, TOF camera possesses ideal features of being deployed in real-time applications: captures a complete scene with one shot and speeds up to 40 frames per second (fps).…”

Section: Fig1: Two Stages Renderingmentioning

confidence: 99%

Occlusion Handling Method for Ubiquitous Augmented Reality Using Reality Capture Technology and GLSL

Dong¹,

Feng²,

Kamat³

2011

Computing in Civil Engineering (2011)

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ABSTRACT:The primary challenge in generating convincing Augmented Reality (AR) graphics is to project 3D models onto a user's view of the real world and create a temporal and spatial sustained illusion that the virtual and real objects co-exist. Regardless of the spatial relationship between the real and virtual objects, traditional AR graphical engines break the illusion of co-existence by displaying the real world merely as a background, and superimposing virtual objects on the foreground. This research proposes a robust depth sensing and frame buffer algorithm for handling occlusion problems in ubiquitous AR applications. A high-accuracy Time-of-flight (TOF) camera is used to capture the depth map of the real-world in real time. The distance information is processed using the OpenGL Shading Language (GLSL) and rendered into the graphics depth buffer, allowing accurate depth resolution and hidden surface removal in composite AR scenes. The designed algorithm is validated in several indoor and outdoor experiments using the SMART AR framework.

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A Time-Of-Flight Depth Sensor - System Description, Issues and Solutions

Cited by 320 publications

References 9 publications

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

A Long-Range Touch Interface for Interaction with Smart TVs

Occlusion Handling Method for Ubiquitous Augmented Reality Using Reality Capture Technology and GLSL

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