theobslhc / ICPCUDA

Super fast implementation of ICP in CUDA for compute capable devices 2.0 or higher

ICPCUDA

Super fast implementation of ICP in CUDA for compute capable devices 2.0 or higher. On an nVidia GeForce GTX TITAN X it runs at over 750Hz (using projective data assocation). To compile all architectures you'll need CUDA 7.0 I think, (or 6.5 with the special release for 9xx cards). You can compile for older cards by removing the unsupported architectures from the CMakeLists.txt file.

Requires CUDA, Pangolin (Optional for the library), Eigen and Sophus. I've built it to take in raw TUM RGB-D datasets to do frame-to-frame dense ICP as an example application.

The particular version of ICP implemented is the one introduced by KinectFusion. This means a three level coarse-to-fine registration pyramid, from 160x120 to 320x240 and finally 640x480 image sizes, with 4, 5 and 10 iterations per level respectively.

Build instructions

mkdir build
cd build
cmake .. ## use -DCMAKE_INSTALL_PREFIX=<path> to install library in a custom location
make

# install library
make install
# uninstall
make uninstall

Example application

Run like;

./ICP ~/Desktop/rgbd_dataset_freiburg1_desk/ -v

Where ~/Desktop/rgbd_dataset_freiburg1_desk/ contains the depth.txt file, for more information see here.

The main idea to getting the best performance is determining the best thread/block sizes to use. I have provided an exhaustive search function to do this, since it varies between GPUs. Simply pass the "-v" switch to the program to activate the search. The code will then first do a search for the best thread/block sizes and then run ICP and output something like this on an nVidia GeForce GTX TITAN X;

GeForce GTX TITAN X
Searching for the best thread/block configuration for your GPU...
Best: 256 threads, 96 blocks (1.3306ms), 100%
ICP: 1.3236ms
ICP speed: 755Hz

The code will output one file; output.poses. You can evaluate it on the TUM benchmark by using their tools. I get something like this;

python ~/stuff/Kinect_Logs/Freiburg/evaluate_ate.py ~/Desktop/rgbd_dataset_freiburg1_desk/groundtruth.txt output.poses
0.144041

The difference in values comes down to the fact that each method uses a different reduction scheme and floating point operations are not associative.

Also, if you're using this code in academic work and it would be suitable to do so, please consider referencing some of my possibly relevant research in your literature review/related work section.