alvinwan / pcmatch

Iterative closest point (ICP) to match point clouds to templates

Point Cloud Matching

Uses iterative closest point (ICP) to match sample point clouds to templates. Includes utilities to convert existing .stl, .obj, .xaml, .pkl etc. objects into point cloud, numpy arrays. To visualize the results in an interactive viewer, see viewer/.

Install

The project is written in Python 3 and is not guaranteed to successfully backport to Python 2.

(Optional) We recommend setting up a virtual environment.

virtualenv pcm --python=python3
source activate pcm/bin/activate

Say $PCM_ROOT is the root of your repository. Navigate to your root repository.

cd $PCM_ROOT

We need to setup our Python dependencies.

pip install -r requirements.txt

Run

By default, the script looks for sample point clouds in ./data/raw and template point clouds in ./data/templates. All point cloud files are .npy files containing nx3 matrices of x,y,z respectively.

python label.py

Here are full usage instructions:

Usage:
    label.py [options]

Options:
    --template=<path>   Path to templates [default: ./data/templates/*.npy]
    --raw=<path>        Path to unclassified data [default: ./data/raw/*.npy]
    --out=<out>         Path for final results [default: ./out/labels.npy]

pcmatch's Issues

match point cloud to 3D CAD model

Hi!

thank you for sharing this release! Great job!

I would like to use it to match 3D CAD model with point cloud. What's the output of label.py ? Does this 4x4 matrix represent transformation matrix to align two .npy?

Match CAD model with point cloud

Hi,

Can I use this code to match my CAD model with point cloud? Thank you!