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https://github.com/yangjiaolong/Go-ICP

Implementation of the Go-ICP algorithm for globally optimal 3D pointset registration
https://github.com/yangjiaolong/Go-ICP

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Implementation of the Go-ICP algorithm for globally optimal 3D pointset registration

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README 

        
# Go-ICP for globally optimal 3D pointset registration






(A demo video can be found on [here](http://jlyang.org/go-icp/).)



### Introduction



This repository contains the C++ code for the Go-ICP algorithm (with trimming strategy for outlier handling). It is free software under the terms of the GNU General Public License (GPL) v3. Details of the Go-ICP algorithm can be found in our papers:



* J. Yang, H. Li, Y. Jia, *Go-ICP: Solving 3D Registration Efficiently and

Globally Optimally*, International Conference on Computer Vision (__ICCV__), 2013. [PDF](http://jlyang.org/iccv13_go-icp.pdf)



* J. Yang, H. Li, D. Campbell, Y. Jia, *Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration*, IEEE Transactions on Pattern Analysis and Machine Intelligence (__TPAMI__), 2016. [PDF](http://jlyang.org/tpami16_go-icp_preprint.pdf)



Please read this file carefully prior to using the code. Some frequently-asked questions have answers here.



### Compiling



Use cmake to generate desired projects on different platforms.



A pre-built Windows exe file can be found in [this zip file](http://jlyang.org/go-icp/Go-ICP_V1.3.zip).



### Terminology



Data points: points of the source point set to be transformed.



Model points: points of the target point set.



### Notes



* ___Make sure both model and data points are normalized to fit in \[-1,1\]3 prior to running___ (we recommend first independently centralizing the two point clouds to the origin then simultaneously scaling them). The default initial translation cube is \[-0.5,0.5\]3 (see “config_example.txt”).



* The convergence threshold is set on the Sum of Squared Error (SSE) as in the code and the paper. For the ease of parameter setting for different data point numbers, we use Mean of Squared Error (MSE) in the configuration (see “config_example.txt”). We use MSE threshold of 0.001 for the demos. ___Try smaller ones if your registration results are not satisfactory___.



* ___Make sure you tune the trimming percentage in the configuration file properly___,  if there are some outliers in the data pointset (i.e., some regions that are not overlapped by the model pointset). Note that a small portion of outliers may lead to competely wrong result if no trimming is used. Refer to our TPAMI paper for more details.



* ___Do NOT subsample the model points!___ Since we use 3D distance transform for closest distance computation, model point number does not affect running speed. Subsampling the model points may increase the optimal registration error thus slowing down the BnB convergance.



* Building 3D distance transform with (default) 300 discrete nodes in each dimension takes about 20-25s in our experiments. Using smaller values can reduce memory and building time costs, but it will also degrade the distance accuracy.



### Running



Run the compiled binary with following parameters: \ \ \ \ \, e.g. “./GoICP model data 1000 config output”, “GoICP.exe model.txt data.txt

500 config.txt output.txt”.



* \ and \ are the point files of the model and data pointsets respectively. Each point file is in plain text format. It begins with a positive point number N in the first line, followed with N lines of X, Y, Z values of the N points.



* \ indicates the number of down-sampled data points. The code assumes the input data points are randomly ordered and uses the first \ data points for registration. ___Make sure you randomly permute your data points or change the code for some other sampling strategies.___



* \ is the configuration file containing parameters for the algorithm, e.g. initial rotation and translation cubes, convergence threshold and trimming percentage. See “config_example.txt” for example.

  

* \ is the output file containing registration results. By default it contains the obtained 3x3 rotation matrix and 3x1 translation vector only. You can adapt the code to output other results as you wish.



Some sample data and scripts can be found in the /demo folder. 



### Other langueage



A python wrapper by @aalavandhaann can be found at https://github.com/aalavandhaann/go-icp_cython



### Acknowledgments



This implementation uses the nanoflann library, and a simple matrix library written by Andreas Geiger. The distance transform implementation is adapted from the code of Alexander Vasilevskiy.



### Change log

V1.3 (26-Jan-2015)



Implemented the intro-selection algorithm



Fixed some minor issues



V1.2 (12-Jun-2014)



Refined the quick-selection algorithm



Added a deconstructor to distance transform class (Thanks to Nima Tajbakhsh)



V1.1 (21-Apr-2014)



Speeded up Trimmed-GoICP (around 2-5 times experimentally) using a quick-selection algorithm



V1.0 (13-Feb-2014)



First complete version for release