CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: EXTRACTING BUILDING FOOTPRINTS FROM 3D POINT CLOUDS USING TERRESTRIAL LASER SCANNING AT STREET LEVEL Karim Hammoudi, Fadi Dornaika and Nicolas Paparoditis

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 66 equipment was precisely installed by topometry. It allows the gathering of georeferenced 3D laser data with a very high density and also much information about the acquisition (see section 4). Figure 1: Visualization of the 3D point cloud of very high den sity. This raw data represents the building facade acquisition. The black line represents the trajectory of the laser sensor. The laser data are acquired under realistic conditions in dense urban environments. Moreover, the datasets are collected in a particular container related to the laser scanner, particularly in a range of 3D points. In this context, the data must be manipulated with much precaution. Here we will describe the main difficulties associated with 3D data. • The acquisition: The laser sensor data can be represented in two ways; either as a container with data organized lin early in temporal sequences of 201 points (data frames) or like a cloud of 3D points (georeferenced data). The condi tions of acquisition are very variable in realistic and dense urban environments. Mobile objects cause a thickening of the acquired cloud when the vehicle stops. Certain acquired points model an ephemeral surface and could be considered as erroneous points. Moreover, the density of the facades vary according to the speed of the vehicle. • The occlusions: pose a problem for the complete acquisi tion of building facades in urban environments. The occlu sions could be caused by two categories of obstacles, static or dynamic created by man-made and natural objects. The raw cloud may suffer from missing data due to the pres ence of pedestrians, trees, mobile and parked vehicles and many others objects (see figure 2). Alas, this inevitable phe nomenon affects the modeling process. • The laser reflectance: could cause confusions in the 3D data interpretation. Certain points don’t model a physical surface. This effect appears on a retroreflector surface. Ob servations sometimes show an aureole of points around road signs. These dispatched points represent erroneous data. Moreover, certain points model a different surface other than the surface of interest. Sometimes, the beam of the laser ei ther rebounds off of the outside of the window or it passes through the window and models the inside of the dwelling. These scattered points represent erroneous information for the facades modeling. In addition to this, other less frequent effects could arise due to poorly reflective surfaces. • • The redundancy of data: is due to many factors. The ac quisition is continuous even when the vehicle is stopped. We have adopted this strategy to facilitate the acquisition of a large area and to use data as common bases for our dif ferent projects. Therefore, raw laser data may contain many redundant frames. Moreover, due to sensor characteristics (orientation and linear scanning), we could sometimes have up to three acquisitions of the same facade part caused by the graining of the laser beam in the turns. The redundancy of data (points, frames, parts of the facade) presents an in convenience for the feature extraction techniques based on vote schemes or random trials. Figure 2: A street in the city of Paris. The building facade is partially occluded by trees and parked vehicles. Figure 3: Returned intensities of the 2D scans. The redundancy effect appears when the vehicle temporarily stops. The vehicle and the branches seem stretched. This brief description allows us to acknowledge several problems associated with the raw laser data. The 3D data should undergo several preprocessing steps before becoming exploitable. Thus we need a process robust to some outliers and noisy data. 3 PROPOSED APPROACH In this section, we describe our approach which consists of two stages. The first stage focuses on the 3D cloud points preprocess ing. The second stage aims at the building footprint extraction. In this work, we assume that buildings have simple polygonal shapes. 3.1 3D data preprocessing 3.1.1 Partial Altering of redundant points As we have men tioned earlier, the laser sensor constantly sweeps the building fa cade even when the vehicle is stopped. Consequently, the ac quired raw data may contain many redundant frames due to this continuous acquisition. For this reason, we have defined a mea sure between two consecutive frames based on point-to-point dis tances. The redundant frames are thus detected and removed from

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