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JetScan : GPU accelerated portable RGB-D reconstruction system

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JETSCAN

(Currently not maintained)

Please refer to Official enhanced GPU tensor reconstruction system Link for O3D docs

      A standalone portable 3D reconstruction system with Intel realsense and Jetson Nano


Motives

  • Standalone 3D reconstruction system
  • Instant 3D reconstruction
  • Metrically accurate
  • Low power consuming
  • Offline and online (both)
  • Cost effective
  • Modular approach
  • Deep learning integration (3D deep learning for registration)

Hardware built:

Reconstructed results:


Introduction

Jetscan is a portable 3D dense reconstruction system based on

  1. Jetson nano by NVIDIA
  2. Intel Realsense D400 series depth camera
  3. Open3D by INTEL ISL
  4. theNded's CUDA implementation

Schematic sketch


Software Setup


Hardware Setup

Follow instructions in

  1. Hardware_electronics - Electronics setup

  2. Hardware_casing - Case/Closure setup


Follow https://github.com/devshank3/JetScan/blob/master/Software_O3D/README.md for Software Setup

Usage

  • Step 1 : Go to Open3D-for-Jetson/Reconstruction_Jetscan_Hybrid/

  • Step 2 : Edit Main_gui.py

    set your system executable path to Run_MakeFragments,ViewPoseGraph,Run_IntegrateScene

    eg.

     os.system('/home/shank/Projects/3D_processing/jetscan/jetscan-final-edit/Open3D-for-Jetson/build/bin/examples/./Run_MakeFragments')
    
                                                            ⇩⇩⇩ to ⇩⇩⇩                       
    
     os.system('your_system _path/Open3D-for-Jetson/build/bin/examples/./Run_MakeFragments')
    
  • Step 3: Run the UI (in virtual env if setup)

    $ python3 Main_gui.py

  • Step 4 : Start the Reconstruction

    Capture RGB-D

    Captures the RGB-D sequence

    Reconstruct Scene

    The hybrid reconstruction system starts with the RGB-D sequence recorded

    View

    View the latest reconstructed 3D Model


Development & Contribute

If interested pls contact me @ [email protected]


License

Under the MIT License (MIT)

Copyright (c) 2020 Shankar Hariharan (aka devshank3)