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KFusion 0.4.1

Copyright TU Graz, Gerhard Reitmayr, 2011 - 2013

This is an implementation sketch of the KinectFusion system described by Richard Newcombe et al. in "KinectFusion: Real-Time Dense Surface Mapping and Tracking", ISMAR 2011, 2011. It is a dense surface reconstruction and tracking framework using a single Kinect camera as the input sensor.

http://research.microsoft.com/en-us/projects/surfacerecon/

KFusion is mainly written in CUDA with some interface code to display graphics output.

Requirements

You need a depth camera: Either a Microsoft Kinect or any camera supported by OpenNI 2 (such as the Asus Xtion Pro Live).

KFusion depends on the following libraries:

On Windows use the MS Kinect SDK:

while on other platforms use either:

and of course the CUDA SDK by NVidia

Install

Use CMake to create build files for your platform. Some tips and tricks

  • On Windows, make sure to use a 64-bit version of GLUT
  • On newer *nix versions you need CLang as host compiler (gcc 5.x upwards is incompatible with nvcc)

Altenatively, On Unix/OSX platforms, tweak the Makefile for your setup, then make.

Have a look at kfusion.h for a description of most parameters and kinect.cpp for setting them.

Todo

  • rendering
    • integrate with GL for additional 3D graphics
  • write an inverse tracking method that moves the camera in the system
  • save size through combined depth + 2D normal maps

Done

  • MSKinect SDK interface for Windows, libfreenect on other platforms
  • rendering with static model view + projected RGB + interactive viewpoint
  • registered depth input from libfreenect, uses more time unfortunately
  • integration speed up
  • CMake build system (contributed by Hartmut Seichter)
  • fixed a substantial bug in tracking
  • improved raycasting by an implementation closer to the paper. This also seems to take care of the following issue:
    • tracking works much better with a detailed model and sharp bounds on normals (0.9), problem for low resultion ?
  • replaced libcvd with GLUT in the master branch
  • created dedicated Image class templated on different memory locations, reduces most dependencies on libcvd
  • removed all 3D grids to reduce code and maybe speed up as well
  • 2D grid for volume integration is 40% faster
  • fixed a difference in computing normals in raycasting and from kinect input... maybe influences tracking ?
  • added a combined tracking & reduce implementation that saves a bit of time
  • make configuration more automatic, compute dependent variables, scales for tracking
  • make a version that uses one volume for both SDF + weight
    • split 32bit into 16bit float [-1,-1] and unsigned int for weight
  • scaling for better optimization, didn't really do much, removed it again
  • multi level tracking
  • split
    • core operations
    • testing with extra volume etc
    • rendering
    • make OO ?
  • speed up raycasting with mu step sizes
  • reduction somewhat better through local memory -> shared memory
  • test larger volume sizes
    • 3D operations over 2D grid - works nicely !
  • pitched images no change, because they are already all pitched !
  • template volume on data type and convert to/from float on the fly - did not change speed at all !
  • ambient lighting
  • bilateral filtering

Contributors

Hartmut Seichter Niklas Hambüchen