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structured light sensor  (PrimeSense Ltd)

 
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    PrimeSense Ltd structured light sensor
    Structured Light Sensor, supplied by PrimeSense Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/structured+light+sensor/structured+light+sensor/pmc06165249-29-19-26
    Average 90 stars, based on 1 article reviews
    structured light sensor - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: A Versatile Method for Depth Data Error Estimation in RGB-D Sensors
    Article Snippet: Several devices can be used nowadays to determine the depth, such as stereo cameras (Bumblebee, ZED, and Minoru 3D), structured light sensors (Kinect v1, Xtion Pro, PrimeSense, and RealSense) and devices with Time of Flight (ToF) technology (Kinect v2, LIDAR).



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    Image Search Results


    Structured light sensor analyzed: ( a ) Einscan-Sp by Shining 3D, ( b ) Leo by Artec 3D.

    Journal: Materials

    Article Title: Optical Characterization of Materials for Precision Reference Spheres for Use with Structured Light Sensors

    doi: 10.3390/ma16155443

    Figure Lengend Snippet: Structured light sensor analyzed: ( a ) Einscan-Sp by Shining 3D, ( b ) Leo by Artec 3D.

    Article Snippet: The other structured light sensor analyzed is a Leo (Artec 3D, Luxembourg) [ ], shown in b, whose main features are presented in .

    Techniques:

    Non-contact sensors’ technology classification: SL = Structured-Light, ToF = Time-of-Flight, LiDAR = Light Detection and Ranging, CCD = charge coupled device.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

    doi: 10.3390/s20247252

    Figure Lengend Snippet: Non-contact sensors’ technology classification: SL = Structured-Light, ToF = Time-of-Flight, LiDAR = Light Detection and Ranging, CCD = charge coupled device.

    Article Snippet: 2014 , Tahavori et al. [ ] , Respiratory motion , D: multi-ROI analysis, to investigate the dominate variations using PCA, based on depth data from a structured light sensor. V: multi-ROI analysis performed on 3 separate sessions. S: 20 subjects. , The first principal component describes more than 70% of the motion data variance in thoracic and abdominal surfaces. , Clinical environment ▪ Abnormality detection. ▪ Analysis of disease severity..

    Techniques:

    Comparison of depth sensors.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

    doi: 10.3390/s20247252

    Figure Lengend Snippet: Comparison of depth sensors.

    Article Snippet: 2014 , Tahavori et al. [ ] , Respiratory motion , D: multi-ROI analysis, to investigate the dominate variations using PCA, based on depth data from a structured light sensor. V: multi-ROI analysis performed on 3 separate sessions. S: 20 subjects. , The first principal component describes more than 70% of the motion data variance in thoracic and abdominal surfaces. , Clinical environment ▪ Abnormality detection. ▪ Analysis of disease severity..

    Techniques: Comparison

    Recent RGB-D sensors: structured-light (Kinect v1, Asus Xtion Pro, Intel SR300), time-of-flight (Kinect v2, Kinect Azure DK, VicoVR), and active stereo vision (Intel R200, D435).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

    doi: 10.3390/s20247252

    Figure Lengend Snippet: Recent RGB-D sensors: structured-light (Kinect v1, Asus Xtion Pro, Intel SR300), time-of-flight (Kinect v2, Kinect Azure DK, VicoVR), and active stereo vision (Intel R200, D435).

    Article Snippet: 2014 , Tahavori et al. [ ] , Respiratory motion , D: multi-ROI analysis, to investigate the dominate variations using PCA, based on depth data from a structured light sensor. V: multi-ROI analysis performed on 3 separate sessions. S: 20 subjects. , The first principal component describes more than 70% of the motion data variance in thoracic and abdominal surfaces. , Clinical environment ▪ Abnormality detection. ▪ Analysis of disease severity..

    Techniques: