-
This is the reimplementation of Iterative Hough Transform for Line Detection in 3D Point Clouds. This implementation organizes the algorithm into a library that can be used in plug-and-play style easily.
-
Image of semihemisphere whose vertices show directions for the method's parameter space discretization
- Implementation of Iterative Hough Transform for Line Detection in 3D Point Clouds
- Test on pointcloud data
sudo apt-get install \
libpcl-dev \make default # to build the library
make apps # to build the examples# build
docker build -f ./dockerfiles/ubuntu2004.dockerfile -t 3d_line_detection .
# run
docker run -it --rm --net host -e DISPLAY=$DISPLAY -v $HOME/.Xauthority:/root/.Xauthority -v `pwd`:/workspace 3d_line_detection- draw semihemisphere
# ./build/examples/draw_sphere_app [number/of/subdivision]
# eg: ./build/examples/draw_sphere_app 0
# number of subdivision: 0 -> 5- test 3d line detection example
# ./build/examples/3d_line_detection_app [path/to/pcd/file] [use/default/setting/yes:1/no:0] [num/range/bin] [sphere/granularity] [min/num/vote] [distance/to/line/thresh]
# eg: ./build/examples/3d_line_detection_app ./data/test_cloud.pcd 0 64 4 10 0.2
# note that if you increase number of accumulator cells, you need to reduce the threshold of minimum vote number| original pointcloud | detected 3d lines |
 |
 |