Time-of-Flight and Structured Light Depth Cameras

Zanuttigh, Pietro; Marin, Giulio; Mutto, Carlo Dal; Dominio, Fabio; Minto, Ludovico; Cortelazzo, G.M.

doi:10.1007/978-3-319-30973-6

Cited by 85 publications

(58 citation statements)

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“…This task is very challenging using color data alone, but recent research has demonstrated that depth data allow a very reliable solution [40]. Furthermore depth data directly provide the position of the hand in 3D space, a fundamental information for the applications of Section 5.…”

Section: Gesture Recognition Modulementioning

confidence: 99%

Head-mounted gesture controlled interface for human-computer interaction

Memo

Zanuttigh

2016

Multimed Tools Appl

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This paper proposes a novel human-computer interaction system exploiting gesture recognition. It is based on the combined usage of an head-mounted display and a multi-modal sensor setup including also a depth camera. The depth information is used both to seamlessly include augmented reality elements into the real world and as input for a novel gesture-based interface. Reliable gesture recognition is obtained through a real-time algorithm exploiting novel feature descriptors arranged in a multi-dimensional structure fed to an SVM classifier. The system has been tested with various augmented reality applications including an innovative human-computer interaction scheme where virtual windows can be arranged into the real world observed by the user

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Section: Gesture Recognition Modulementioning

confidence: 99%

Head-mounted gesture controlled interface for human-computer interaction

Memo

Zanuttigh

2016

Multimed Tools Appl

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“…On the other side, ToF cameras represent a quite robust solution for depth acquisition [16,17,2,18,19,20]. The various low cost depth cameras available on the market can acquire depth information in real-time and are more robust to the scene content with respect to stereo systems, in particular they can estimate the depth also in regions without texture or with repetitive patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Several different approaches for the combination of stereo and ToF data have been proposed. Comprehensive reviews of the topic can be found in [26] and [2].…”

Section: Related Workmentioning

confidence: 99%

“…An effective solution for this task needs two fundamental tools: the estimation of the reliability of the data acquired by the two devices at each location and a fusion algorithm that uses this information to properly combine the two data sources. The reliability of ToF data has been traditionally estimated by modeling the noise of such sensors [2]. ToF sensors are typically affected by various sources of error.…”

Section: Introductionmentioning

confidence: 99%

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Stereo and ToF data fusion by learning from synthetic data

et al. 2019

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Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods.

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“…The proposed solution is based on Kinect like sensors [12], which are sensors that provide RGB or coloured images and depth images. However, the used sensor is a Structure camera from Occipital, which only provides infra-red (IR) and depth images, [13].…”

Section: A Sensorsmentioning

confidence: 99%

Remote inspection with multi-copters, radiological sensors and SLAM techniques

et al. 2018

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Abstract-Activated material can be found in different scenarios, such as in nuclear reactor facilities or medical facilities (e.g. in positron emission tomography commonly known as PET scanning). In addition, there are unexpected scenarios resulting from possible accidents, or where dangerous material is hidden for terrorism attacks using nuclear weapons. Thus, a technological solution is important to cope with fast and reliable remote inspection. The multi-copter is a common type of Unmanned Aerial Vehicle (UAV) that provides the ability to perform a first radiological inspection in the described scenarios. The paper proposes a solution with a multi-copter equipped with on-board sensors to perform a 3D reconstruction and a radiological mapping of the scenario. A depth camera and a Geiger-Müler counter are the used sensors. The inspection is performed in two steps: i) a 3D reconstruction of the environment and ii) radiation activity inference to localise and quantify sources of radiation. Experimental results were achieved with real 3D data and simulated radiation activity. Experimental tests with real sources of radiation are planned in the next iteration of the work.

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Time-of-Flight and Structured Light Depth Cameras

Cited by 85 publications

References 0 publications

Head-mounted gesture controlled interface for human-computer interaction

Head-mounted gesture controlled interface for human-computer interaction

Stereo and ToF data fusion by learning from synthetic data

Remote inspection with multi-copters, radiological sensors and SLAM techniques

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