Features of Point Clouds and Functionalities of Processing Software Mathias J.P.M. Lemmens Delft University of Technology, The Netherlands (MSc Geomatics for the Built Environment: (1) Geodata Acquisition Technology & (2) Geodata Quality) Senior Editor GIM International International Consultant (2014 -2015: WB, Kenya) M.J.P.M.Lemmens@tudelft.nl

“It’s very encouraging to know that the community is so big.” GIM International senior editor Mathias Lemmens is guiding his TU Delft students through Intergeo, Berlin, 2014. “The first day our professor guided us along a variety of booths and showed us how to fearlessly shoot questions and ask for demonstrations.”

Published by Springer, 2011

Nucleus of Point Clouds

Shukhov Tower Moscow, Russia Built in 1919-1922, radio broadcasting Monument of Russian avant-garde architecture 100 million points accuracy: 7mm Mikhail Anikushkin & Andrey Leonov, Russia, 3D Modelling of Shukhov Tower3D Modelling of Shukhov Tower, GIM Int’l, July 2014

Mobile Mapping System Terrasolid, Road Maintenance with MMS, pilot in Finland in 2012, GIM Int’l, July 2014 Section of 22 km of the two-lane NR6 (Finland) with Trimble MX8: 2 Riegl VQ-250 scanners 4 cameras – 1 recording road surface and 3pointing forward Applanix POS LV 520 MMS data processed with sophisticated software is well suited for road maintenance (+ machine steering) tasks as many parameters can be accurately calculated from the 3D model.

Airborne Lidar Netherlands 40% below sea-level Threats from Sea, Germany (Rhine) and France (Meuse) Height Model 1998: 1 pnt / 25 square meters 2013: 10 pnts / square meter On Average doubling of number of points every two years.

Aerial multi camera systems capture oblique and nadir imagery at the same time  full and intuitive view on both building footprints and facades beneficial for creating 3D city models. Object identification and creation of dense 3D point clouds are easier and more reliable compared to conventional vertical imagery although the cost of capturing is higher.

Dense image matching allows point densities similar to the ground sampling distance (GSD) of the imagery from which they are derived. (e.g. GSD of 10cm  100 height points per square meter. Semi-global matching (SGM) algorithm introduced by Hirschmüller (2008) Challenging for oblique imagery: - large scale variations - illumination changes - many occlusions Need for performance measures of DIM software

Point cloud generation M. Deuber, S. Cavegn, S. Nebiker (Switzerland) Dense Image Matching – Performance Analysis on Oblique Imagery, GIM Int’l, Sept. 2014. Task Photo- Scan Stereo- SGBM SURE Xpro SGM Image rectification V   Image matching Point cloud generation DSM computation DSM texturing

Completeness Ratio between number of pixels to which software assigns a depth value and total number of pixels.

What to do with all those points?  Massive Point Clouds Everywhere What to do with all those points?  Benchmark EuroSDR Survey - Oblique Airborne Photogrammetry: Users’ and Vendor’s View Markus Gerke, University of Twente, The Netherlands Fabio Remondino, FBK Trento, Italy (To be published in GIM Int’l Dec. 2014) Massive Point clouds for eSciences; Delft University of Technology; Rijkswaterstaat; Netherlands eScience Centre; Fugro and Oracle (Submitted to Computers & Graphics)

What can obliques do better? 130 Respondents; Universities 45%, National Mapping Agencies 21% Questionnaire still open https://www.surveymonkey.com/s/EuroSDR_oblique EuroSDR Survey What can obliques do better?

Massive Point Clouds for eSciences Point clouds are too massive for efficient handling by common Geo-ICT infrastructures User requirements by structured interviews with users. Based on requirements a benchmark has been designed by comparing the loading and querying of data sets consisting of 20 million, 20 billion and 640 billion points Oracle, PostgreSQL, MonetDB and LAStools. Proposals for storage improvement and thus accessibility

Sources of Point Clouds Millions, billions, trillions of 3D points Airborne Lidar Overlapping imagery (Dense Image Matching) Terrestrial Laser Scanning / Mobile Mapping Spaceborne and Airborne Radar (e.g. Terrasat) Sonar

Functionalities Of Point Cloud Processing software

Software General purpose: handle point clouds from a diversity of sensors (e.g. Terrasolid) Dedicated to specific output e.g. TLS, airborne Lidar, MMS or sonar (proprietary software) Focus may be on a broad pallet of end products from a particular sensor type Other end of the spectrum: application domains. Constructor using CAD exploits TLS point clouds and want modules for processing them. Dedicated modules on top of base modules, e.g. mining industry or 3D models of crash sites.

Development far from complete New tools are being added all the time A generic package for all types of sensor outputs end products does not exist Look at functionalities but also examine design ideas, current or planned extensions, its ability to join modules into one workflow, and interoperability with other software and services.

To compare Point Cloud Processing Software have a look at the successor of GIM’s Product Surveys

Point Clouds – Acquisition, Processing and Management to be published by Whittless Publishing, UK, in 2016. To be presented at ISPRS Congress, 2016, Prague.

Thank you so much for your attention.