Microsoft Kinect Sensor and Its Effect

By Zhengyou Zhang. 2012

2,561 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Sergey Levine
2. Ming Liu
3. Ming-Ming Cheng
4. A. Bronstein
5. G. Wang
6. Fereshteh Sadeghi
7. Or Litany
8. Liang Wang

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. G. Wang
4. Dacheng Tao
5. Xiaogang Wang
6. Jun Liu
7. Yi Yang
8. Jitendra Malik

In Human-Computer Interaction

1. Jun Liu
2. Liang Wang
3. Hossein Rahmani
4. G. Wang
5. Deng-Ping Fan
6. Amir Shahroudy
7. Oscar Koller
8. Mengyuan Liu

Citations in the corpus

• NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, methodology): “After the release of Microsoft Kinect [48], several datasets are collected by different groups to perform research on 3D action recognition and to evaluate different methods in this field.”
• NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, methodology): “After the release of the Microsoft Kinect [37], several datasets have been collected to conduct research on 3D human action recognition and to evaluate different methods in this field.”
• Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, methodology): “All these datasets are collected using the Microsoft Kinect sensor in an indoor environment [86].”
• View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017, background): “Besides, the prevalence of cost-effective depth cameras such as Microsoft Kinect [48], Intel RealSense [1], dual camera devices, and the advance of a powerful technique of human pose estimation from depth [34] make 3D skeleton data easily obtainable.”
• (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “mapping and localization techniques based on 3D perception, including SLAM [3], depth sensors [44], stereo cameras [37], and monocular cameras using structure from motion [8].”
• An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data (2016, background): “The prevalence of cost-effective depth cameras such as Microsoft Kinect (Zhang 2012) and the advance of a powerful human pose estimation technique from depth (Shotton et al. 2011) make 3D skeleton data easily accessible.”
• Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, methodology): “3D acquisition technology has made great progress in the last decade, and is being rapidly incorporated into commercial products ranging from Microsoft Kinect [43] for gaming, to LIDARs used in autonomous cars.”
• Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Lun and Zhao [19] gave a survey of HAR using Microsoft Kinect [28] with the template-based and algorithmic-based”
• An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking (2014, background): “In combination with an open SDK, it can be used to acquire and recognize full body gestures for multiple users at a time [5].”
• Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016, methodology): “With the emergence of consumer depth cameras [5], researchers quickly incorporated depth sensors into their systems, as depth simplifies the task of human pose estimation [6].”
• RGBD Salient Object Detection via Deep Fusion (2016, positive): “Recently emerging sensing technologies, such as Time-of-Flight sensors and the Microsoft Kinect, provide excellent capability and flexibility in capturing RGBD images [5], [6].”
• An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition (2019, background): “[42] and advanced human pose estimation algorithms [3] make it easier to gain skeleton data.”
• Recognizing Human Actions as the Evolution of Pose Estimation Maps (2018, neutral): “, Microsoft Kinect [55, 27].”
• KinectFaceDB: A Kinect Database for Face Recognition (2014, background): “This may occur for the several reasons: 1) the point is too far (out of the sensing range); 2) the point is too close (which lies in a blind region due to the limited field of view for the projector and the IR camera); 3) the point is in shadow cast by the projector (where the IR light cannot reach)”
• A Survey of Augmented Reality (2015, background): “In recent years, commercial sensors capable of detecting 3D structure information from the environment have become very a ordable, beginning with the introduction of the Microsoft Kinect [Zhang, 2012].”
• SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters (2018, background): “With the recent proliferation of applications employing 3D depth sensors [23] such as autonomous navigation, robotics and virtual reality, there is an”