Awesome

KFusion 0.4

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

KFusion depends on the following libraries:

• http://www.edwardrosten.com/cvd/toon.html
• GLUT

On Windows use the MS Kinect SDK:

• http://www.microsoft.com/en-us/kinectforwindows/develop/overview.aspx

while on other platforms use libfreenect:

• http://openkinect.org/

and of course the CUDA 5 SDK by NVidia

• http://developer.nvidia.com/cuda

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 Apple OSX, set CUDA_HOST_COMPILER to /usr/bin/g++

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