https://github.com/mp3guy/icpcuda

Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher
https://github.com/mp3guy/icpcuda

cuda icp

Last synced: 4 days ago
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Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher

• Host: GitHub
• URL: https://github.com/mp3guy/icpcuda
• Owner: mp3guy
• License: bsd-3-clause
• Created: 2015-03-23T14:17:21.000Z (almost 10 years ago)
• Default Branch: master
• Last Pushed: 2021-10-06T11:14:47.000Z (over 3 years ago)
• Last Synced: 2025-01-25T10:05:55.073Z (11 days ago)
• Topics: cuda, icp
• Language: C++
• Homepage:
• Size: 88.9 KB
• Stars: 710
• Watchers: 32
• Forks: 179
• Open Issues: 17
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ICPCUDA

Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher. On an NVIDIA GeForce GTX TITAN X it runs at over __750Hz__ (using projective data assocation). Last tested with Ubuntu 18.04.2, CUDA 10.1 and NVIDIA drivers 418.39.

Requires CUDA, includes [Pangolin](https://github.com/stevenlovegrove/Pangolin), [Eigen](https://github.com/stevenlovegrove/eigen) and [Sophus](https://github.com/stevenlovegrove/Sophus) third party submodules. I've built it to take in raw TUM RGB-D datasets to do frame-to-frame dense ICP as an example application.

Install;

```bash

sudo apt-get install build-essential cmake libglew-dev libpng-dev

git clone https://github.com/mp3guy/ICPCUDA.git

cd ICPCUDA

git submodule update --init

cd third-party/Pangolin/

mkdir build

cd build/

cmake ../ -DEIGEN_INCLUDE_DIR=

make -j12

cd ../../../

mkdir build

cd build/

cmake ..

make -j12

```

The particular version of ICP implemented is the one introduced by [KinectFusion](http://homes.cs.washington.edu/~newcombe/papers/newcombe_etal_ismar2011.pdf). 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. 

Run like;

```bash

./ICP ~/Desktop/rgbd_dataset_freiburg1_desk/ -v

```

Where ~/Desktop/rgbd\_dataset\_freiburg1\_desk/ contains the depth.txt file, for more information see [here](http://vision.in.tum.de/data/datasets/rgbd-dataset).

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;

```bash

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;

```bash

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](https://halshs.archives-ouvertes.fr/hal-00949355v1/document).

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](http://www.thomaswhelan.ie/#publications) in your literature review/related work section.