AUTHORS : antoine.hacquard, celian.gossec, nicolas.ceccarello, nathan.coupermant
For this project, we were tasked with implementing an ICP (Iterative Closest Point) algorithm in CUDA. The project was split in different steps:
- First, work on getting a valid CPU implementation
- Benchmark the CPU implementation to have a baseline
- Work on getting a (better) GPU implementation
- Benchmark the GPU implementation
- Repeat steps 3 and 4, and add optimizations Libraries were authorized but not encouraged, as few libraries work in CUDA.Using a library meant going from CPU to GPU would be more complicated.
- Get the eigen library with :
wget https://gitlab.com/libeigen/eigen/-/archive/3.3.8/eigen-3.3.8.zip - Unzip it in the directory root :
unzip eigen-3.3.8.zip - Compile the code and create a binary in the relevant directory:
make -j4 cpu # compile the CPU version of the ICP in the CPU directory
make -j4 gpu # compile the non optimized GPU version in the GPU directory
make -j4 gpu-opti # Compile the optimize GPU version in the GPU-opti directory
./{directory-where-you-create-the-binary}/icp datatest/{cloud-points}_src datatest/{cloud-points}_tgt