ICP

Iterative closest point (ICP) is an algorithm employed to minimize the difference between two clouds of points. This code is used to reconstrct 3d surfaces, and final result is shown by a trimesh-class-object.

The main steps for ICP is as list:

1. select a subject from source points (src-pts).
2. match each src-pts to closest destination points (dst-pts) by knn.
3. reject bad corresponding pairs.
4. solve optimization function to get homograph transformation matrix.
5. align src-pts to dst-pts, and then iterate.

For the rejection part, I reject all of bad corresponding paires which the angle residual of their normal vector is more than 20°. If you want to change this, please use ctrl+f in core code to find key-words threshold to change it.

Requirements

The version of python is 3.6.12, and the libraries I used as following,

• open3d==0.12.0
• scikit-learn==0.24.1
• transformations==2020.1.1
• trimesh==3.9.1
• numpy==1.19.2
• matplotlib==3.3.2
• argparse==1.4.0

File structure

• data this folder is used to place our resources files for testing our algorithm.
• docs the report of my experiment.
• src includes all of the source code.
  • part*.py in this files, I've done several experiment (check the report, you'll understand what I've done).
  • tools floder is a package I build to implement core algorithm
    • baseICP.py - point to point ICP
    • normalICP.py - point to plane ICP
    • tools.py - some tool function, like show mesh in open3d gui

Easy using

Fistly, you should import the nessary module.

import trimesh
import tools.baseICP  # our python package
import tools.normalICP

And then load two meshes. Our aim is to trasform src_mesh to align with dst_mesh

dst_tm = trimesh.load(dst_mesh_fpth)
src_tm = trimesh.load(src_mesh_fpth)

Use our ICP method to compute transformation matrix H (4*4).

# MeanErrors is a list which store the mean error of each iteration.
# H, MeanErrors = tools.normalICP.icp(src_tm, dst_tm, max_iterations=30)
H, MeanErrors = tools.baseICP.icp(src_tm, dst_tm, max_iterations=30)

Experiments

You can read the code of my experiments to find out the usage of ICP function. And more experiment details can be found in this report.

For run these experiment code, you should make sure all of dependencies have been build. And next,

cd src/

For experiment 1, see the ICP's performance:

# see the result by open3d
# remember to press ‘q’ in keyboard to see the next plot
python part1.py
	
# if you want to see the result by matplotlib
python part1.py -plt

For experiment 2, simulate the effect of increasing misalignment by adding a rotation.

python part2.py

For experiment 3, Evaluate how well ICP performs as you continue to add more noise.

python part3.py

For experiment 4, report accuracy with increasing subsampling rates.

python part4.py

For experiment 5, reconstruction from all of scan models.

python part5.py

For experiment 6, implement point to plane ICP.

python part6.py

See a sample

If you run the part5 code, you will get the result as following.