Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera

Low-cost RGB-D cameras are increasingly being used in several research fields, including human–machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160–10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100–1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.

[1]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[2]  Filiberto Chiabrando,et al.  Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera , 2009, Sensors.

[3]  Jianguo Liu,et al.  Precise Subpixel Disparity Measurement From Very Narrow Baseline Stereo , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Juho Kannala,et al.  Joint Depth and Color Camera Calibration with Distortion Correction , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Sotiris Makris,et al.  Intuitive dual arm robot programming for assembly operations , 2014 .

[7]  Jan Boehm Accuracy Investigation for Structured-light Based Consumer 3D Sensors , 2014 .

[8]  Tao Zhang,et al.  Robust RGB-D simultaneous localization and mapping using planar point features , 2015, Robotics Auton. Syst..

[9]  Elise Lachat,et al.  Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling , 2015, Remote. Sens..

[10]  Gabriele Guidi,et al.  3D CAPTURING PERFORMANCES OF LOW-COST RANGE SENSORS FOR MASS-MARKET APPLICATIONS , 2016 .

[11]  Jürgen Ziegler,et al.  Contactless heart rate variability measurement by IR and 3D depth sensors with respiratory sinus arrhythmia , 2017, ANT/SEIT.

[12]  Luca Di Angelo,et al.  Automatic dimensional characterisation of pottery , 2017 .

[13]  Anders Grunnet-Jepsen,et al.  Intel(R) RealSense(TM) Stereoscopic Depth Cameras , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[14]  Monica Carfagni,et al.  Fast and low cost acquisition and reconstruction system for human hand-wrist-arm anatomy , 2017 .

[15]  Monica Carfagni,et al.  On the Performance of the Intel SR30 Depth Camera: Metrological and Critical Characterization , 2017, IEEE Sensors Journal.

[16]  Ji Yoon Ahn,et al.  MOYA: Interactive AI toy for children to develop their language skills , 2018, AH.

[17]  Jafar Saniie,et al.  AR Marker Aided Obstacle Localization System for Assisting Visually Impaired , 2018, 2018 IEEE International Conference on Electro/Information Technology (EIT).

[18]  Chih-Peng Fan,et al.  3D Depth Information Based 2D Low-Complexity Hand Posture and Gesture Recognition Design for Human Computer Interactions , 2018, 2018 3rd International Conference on Computer and Communication Systems (ICCCS).

[19]  Mark Asselin,et al.  Towards webcam-based tracking for interventional navigation , 2018, Medical Imaging.

[20]  Song Zhang,et al.  High-speed 3D shape measurement with structured light methods: A review , 2018, Optics and Lasers in Engineering.

[21]  Maria Kyrarini,et al.  RGB-D Camera based 3D Human Mouth Detection and Tracking Towards Robotic Feeding Assistance , 2018, PETRA.

[22]  Vinayak Ashok Prabhu,et al.  Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies , 2018 .

[23]  Francesco Luke Siena,et al.  Utilising the Intel RealSense Camera for Measuring Health Outcomes in Clinical Research , 2018, Journal of Medical Systems.

[24]  Thomas P. Kersten,et al.  COMPARATIVE GEOMETRICAL ACCURACY INVESTIGATIONS OF HAND-HELD 3D SCANNING SYSTEMS – AN UPDATE , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[25]  H. Aoki,et al.  Study on Non-contact Heart Beat Measurement Method by Using Depth Sensor , 2018, IFMBE Proceedings.

[26]  Rocco Furferi,et al.  A practical methodology for computer-aided design of custom 3D printable casts for wrist fractures , 2020, The Visual Computer.

[27]  Timothy K. Shih,et al.  Real-Time Static and Dynamic Gesture Recognition Using Mixed Space Features for 3D Virtual World's Interactions , 2018, 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[28]  Luca Di Angelo,et al.  An AHP-based method for choosing the best 3D scanner for cultural heritage applications , 2018, Journal of Cultural Heritage.

[29]  Jing Li,et al.  Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense , 2018, 2018 Eighth International Conference on Information Science and Technology (ICIST).

[30]  Remo Sala,et al.  A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight, Structured-Light and Active Stereoscopy Technologies , 2018, SpringerBriefs in Computer Science.

[31]  Paula Gardner,et al.  Your Body of Water: A Display that Visualizes Aesthetic Heart Rate Data from a 3D Camera , 2018, TEI.

[32]  Jennifer C. Ricklin,et al.  Autonomous Systems: Sensors, Vehicles, Security, and the Internet of Everything , 2018 .

[33]  Bin Sheng,et al.  Deep gesture interaction for augmented anatomy learning , 2019, Int. J. Inf. Manag..