Literature DB >> 31094683

Intel® RealSense™ SR300 Coded light depth Camera.

Aviad Zabatani, Vitaly Surazhsky, Erez Sperling, Sagi Ben Moshe, Ohad Menashe, David H Silver, Tzachi Karni, Alexander M Bronstein, Michael M Bronstein, Ron Kimmel.   

Abstract

Intel® RealSense™ SR300 is a depth camera capable of providing a VGA-size depth map at 60 fps and 0.125mm depth resolution. In addition, it outputs an infrared VGA-resolution image and a 1080p color texture image at 30 fps. SR300 form-factor enables it to be integrated into small consumer products and as a front facing camera in laptops and Ultrabooks. The SR300 depth camera is based on a coded-light technology where triangulation between projected patterns and images captured by a dedicated sensor is used to produce the depth map. Each projected line is coded by a special temporal optical code, that enables a dense depth map reconstruction from its reflection. The solid mechanical assembly of the camera allows it to stay calibrated throughout temperature and pressure changes, drops, and hits. In addition, active dynamic control maintains a calibrated depth output. An extended API LibRS released with the camera allows developers to integrate the camera in various applications. Algorithms for 3D scanning, facial analysis,hand gesture recognition, and tracking are within reach for applications using the SR300. In this paper, we describe the underlying technology, hardware, and algorithms of the SR300, as well as its calibration procedure, and outline some use cases.

Entities:  

Year:  2019        PMID: 31094683     DOI: 10.1109/TPAMI.2019.2915841

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  6 in total

1.  [Accuracy of key point matrix technology based contactless automatic measurement for joint motion of hand].

Authors:  Lulu Lü; Jiantao Yang; Fanbin Gu; Jingyuan Fan; Chaoyang Wang; Qingtang Zhu; Xiaolin Liu
Journal:  Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi       Date:  2022-05-15

2.  Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud.

Authors:  Hui Niu; Takahiro Ito; Damien Desclaux; Ko Ayusawa; Yusuke Yoshiyasu; Ryusuke Sagawa; Eiichi Yoshida
Journal:  J Imaging       Date:  2022-06-13

3.  Classification Algorithm for Person Identification and Gesture Recognition Based on Hand Gestures with Small Training Sets.

Authors:  Krzysztof Rzecki
Journal:  Sensors (Basel)       Date:  2020-12-18       Impact factor: 3.576

Review 4.  Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders.

Authors:  Christina Salchow-Hömmen; Matej Skrobot; Magdalena C E Jochner; Thomas Schauer; Andrea A Kühn; Nikolaus Wenger
Journal:  Front Hum Neurosci       Date:  2022-02-03       Impact factor: 3.169

5.  3D Static Point Cloud Registration by Estimating Temporal Human Pose at Multiview.

Authors:  Byung-Seo Park; Woosuk Kim; Jin-Kyum Kim; Eui Seok Hwang; Dong-Wook Kim; Young-Ho Seo
Journal:  Sensors (Basel)       Date:  2022-01-31       Impact factor: 3.576

6.  An Experimental Assessment of Depth Estimation in Transparent and Translucent Scenes for Intel RealSense D415, SR305 and L515.

Authors:  Eva Curto; Helder Araujo
Journal:  Sensors (Basel)       Date:  2022-09-28       Impact factor: 3.847

  6 in total

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