Efficient Point Cloud Pre-processing using The Point Cloud Library

Marius Miknis, Jonathan Ware, Ross Davies, Peter Plassmann

Research output: Contribution to journalArticlepeer-review

1102 Downloads (Pure)


Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid down sample filtering to simplify point clouds; and pass through filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
Original languageEnglish
Pages (from-to)63-72
Number of pages9
JournalInternational Journal of Image Processing
Issue number2
Publication statusPublished - 1 Jun 2016


  • Point Cloud
  • Point Cloud Library
  • Point Data Pre-processing


Dive into the research topics of 'Efficient Point Cloud Pre-processing using The Point Cloud Library'. Together they form a unique fingerprint.

Cite this