elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Automatic Large-Scale 3D Building Shape Refinement Using Conditional Generative Adversarial Networks

Bittner, Ksenia and d'Angelo, Pablo and Körner, Marco and Reinartz, Peter (2018) Automatic Large-Scale 3D Building Shape Refinement Using Conditional Generative Adversarial Networks. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020", 4.-7. Jun. 2018, Riva del Garda, Italien. DOI: 10.1109/CVPRW.2018.00249

[img] PDF
4MB

Official URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/103/2018/isprs-archives-XLII-2-103-2018.pdf

Abstract

Three-dimensional building reconstruction from remote sensing imagery is one of the most difficult and important 3D modeling problems for complex urban environments. The main data sources provided the digital representation of the Earths surface and related natural, cultural, and man-made objects of the urban areas in remote sensing are the digital surface models (DSMs). The DSMs can be obtained either by light detection and ranging (LIDAR) , SAR interferometry or from stereo images. Our approach relies on automatic global 3D building shape refinement from stereo DSMs using deep learning techniques. This refinement is necessary as the DSMs, which are extracted from image matching point clouds, suffer from occlusions, outliers, and noise. Though most previous works have shown promising results for building modeling, this topic remains an open research area. We present a new methodology which not only generates images with continuous values representing the elevation models but, at the same time, enhances the 3D object shapes, buildings in our case. Mainly, we train a conditional generative adversarial network (cGAN) to generate accurate LIDAR-like DSM height images from the noisy stereo DSM input. The obtained results demonstrate the strong potential of creating large areas remote sensing depth images where the buildings exhibit better-quality shapes and roof form

Item URL in elib:https://elib.dlr.de/120564/
Document Type:Conference or Workshop Item (Speech)
Title:Automatic Large-Scale 3D Building Shape Refinement Using Conditional Generative Adversarial Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Bittner, KseniaKsenia.Bittner (at) dlr.dehttps://orcid.org/0000-0002-4048-3583
d'Angelo, Pablopablo.angelo (at) dlr.deUNSPECIFIED
Körner, Marcomarco.koerner (at) tum.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:May 2018
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Volume:XLII-2
DOI :10.1109/CVPRW.2018.00249
Page Range:pp. 1-6
Publisher:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Status:Published
Keywords:Conditional generative adversarial networks, Digital Surface Model, 3D scene refinement, 3D building shape
Event Title:ISPRS TC II Mid-term Symposium “Towards Photogrammetry 2020"
Event Location:Riva del Garda, Italien
Event Type:international Conference
Event Dates:4.-7. Jun. 2018
Organizer:International Society for Photogrammetry and Remote Sensing
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Bittner, Ksenia
Deposited On:22 Jun 2018 12:35
Last Modified:31 Jul 2019 20:18

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.