Point Cloud Visualization

Video Tutorial:
Author: Light, Camera, Vision!

Setup

Step 1: (Optional) If you don't want to mess up your existing setup, consider creating a conda environment.

PPTK works with Python 3.6

conda create -n LCV_PC_VIS python=3.6
conda activate LCV_PC_VIS

Step 2: (Required)

pip install -r requirements.txt

To read .laz files using laspy in addition to .las files.

python3 -m pip install "laspy[lazrs,laszip]"

Data

Please download the data folder from this link and keep it in the main directory as shown below or change the paths in the code. The data references are provided at the end of this repo.

│Point-Cloud-Visualization/
├──data/
├──.......

What to Expect?

In this turorial, we learn the easy ways to visualize several different point cloud file formats that are commonly used to store point cloud-type information using two very popular python packages (Open3D & pptk - Point Processing Toolkit).

• The list of file formats covered here is below, with references to the popular datasets they are found in.
  • ply (Toronto3D)
  • pcd (Trimble, Toyota PCD datasets)
  • npz, npy (ScanNet, ShapeNet, Sun RGB-D, A2D2-Audi Autonomous Driving Dataset)
  • hdf5 (ModelNet-C, ShapeNet-C, ScanObjectNN)
  • binary (KITTI)
  • las, laz (USGS 3DEP)
  • txt (ModelNet40, Semantic3D)

Misc Useful Links:

1. Open3D Shortcut Control Keys
2. PPTK Shortcut Control Keys

Data References:

1. PLY file: https://github.com/HuangCongQing/Point-Clouds-Visualization/blob/master/2open3D/data/fragment.ply
2. pcd file: https://github.com/PointCloudLibrary/data/tree/master/terrain
3. ShapeNet: https://shapenet.org/
4. ScanObjectNN: https://hkust-vgd.github.io/scanobjectnn/
5. KITTI data: http://www.cvlibs.net/datasets/kitti/index.php
6. las file: vegetation_1_3.las: https://github.com/laspy/laspy/blob/master/tests/data/vegetation_1_3.las
7. laz file: https://github.com/laspy/laspy/blob/master/tests/data/plane.laz
8. ModelNet40: https://modelnet.cs.princeton.edu/

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Feel free to ⭐ this repo if you found this tutorial somewhat helpful. Thanks!
If you have any question, please comment on the YouTube video.

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