This code implements the algorithm described in (cited as "IPOL paper" below):
C. Dalitz, T. Schramke, M. Jeltsch: "Iterative Hough Transform for Line Detection in 3D Point Clouds." Image Processing On Line 7 (2017), pp. 184–196. https://doi.org/10.5201/ipol.2017.208
Please cite this article when using the code. The article was published on IPOL with version 1.1 of this code. For changes since then, see the file CHANGES.
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hough3dlines.cpp
Main program that implements Algorithm 1 (Iterative Hough Transform) of the IPOL paper -
hough.[h|cpp]
Class implementing Algorithm 2 (Hough Transform) of the IPOL paper -
sphere.[h|cpp]
Class implementing the direction discretization as described in section 2.2 of the IPOL paper -
vector3d.[h|cpp]
Class for a 3D point with common math operations -
pointcloud.[h|cpp]
Class for a set of 3D points
The source code is written in C++ and thus requires a C++ compiler. Moreover, it needs the external library libeigen 3.x, available from http://eigen.tuxfamily.org/.
For compilation with the provided Makefile, three adaptions to your environment might be necessary:
- set CC to your C++ compiler
- set LIBEIGEN to the path to your libeigen headers
To compile the program, simply type "make". To test it on the test data, type "make test".
hough3dlines without any or with an unknown option (e.g. "-?") will print the following usage message:
Usage:
hough3dlines [options] <infile>
Options (defaults in brackets):
-o <outfile> write results to <outfile> [stdout]
-dx <dx> step width in x'y'-plane [0]
when <dx>=0, it is set to 1/64 of total width
-nlines <nl> maximum number of lines returned [0]
when <nl>=0, all lines are returned
-minvotes <nv> only lines with at least <nv> points are returned [0]
-gnuplot print result as a gnuplot command
-raw print plot data in easily machine-parsable format
-delim <char> use <char> as field delimiter in input file [,]
-v be verbose and print Hough space size to stdout
-vv be even more verbose and print Hough lines (before LSQ)
The input format is described in section 5 of the IPOL paper. The input file must contain the point coordinates, one point per line with x,y, and z separated by commas. Lines starting with a hash (#) are ignored. Example:
# point cloud data from a wondrous experiment
41.7201,138.2140,-648.0000
0.0001,-138.2140,-440.0000
2.4543,-136.8650,-436.8000
The delimiter (comma) can be cchanged with the command line option "-delim".
The output is printed to stdout unless it is redirected to a file with the option "-o". The standard output format is described in section 5 of the IPOL paper. It lists the detected lines one per line with the number of points and the parameters a and b, as in the following example:
npoints=58, a=(2.5682,-123.8986,0.3456), b=(0.0000,0.7071,0.7071)
npoints=32, a=(-2.4576,34.8665,0.0000), b=(0.7071,0.0000,0.7071)
npoints=3, a=(7.0056,-12.7867,8.5634), b=(0.5773,0.5773,0.5773)
There are two other optional output formats that can be useful for visualization of the result:
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The option "-gnuplot" can be used to immediately visualize the result by piping the program output to gnuplot, as in the following example:
hough3dlines testdata.dat -dx 0.4 -gnuplot | gnuplot -persist
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The option "-raw" prints the plot data in an easily machine-readable format as space separated values that can be used to automatically construct a plotting comand. The numbers have the following meaning:
minX maxX minY maxY minZ maxZ paramMin paramMax aX aY aZ bX bY bZ npoints
where the first line specifies the point cloud bounding box, the second line the range of t values in the line parametrization a + t*b, and the following lines list the line parameters with one line per line.
Christoph Dalitz, 2017-2020, https://lionel.kr.hsnr.de/~dalitz/
Tilman Schramke, Manuel Jeltsch, 2017
Institute for Pattern Recognition, Niederrhein University of Applied Sciences,
Krefeld, Germany
This code is provided under a BSD-style license. See the file LICENSE for details.