Publikation: TreePartNet : neural decomposition of point clouds for 3D tree reconstruction
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
DOI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
We present TreePartNet, a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural neural decomposition exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
LIU, Yanchao, Jianwei GUO, Bedrich BENES, Oliver DEUSSEN, Xiaopeng ZHANG, Hui HUANG, 2021. TreePartNet : neural decomposition of point clouds for 3D tree reconstruction. In: ACM Transactions on Graphics. Association for Computing Machinery (ACM). 2021, 40(6), 232. ISSN 0730-0301. eISSN 1557-7368. Available under: doi: 10.1145/3478513.3480486BibTex
@article{Liu2021TreeP-55977, year={2021}, doi={10.1145/3478513.3480486}, title={TreePartNet : neural decomposition of point clouds for 3D tree reconstruction}, number={6}, volume={40}, issn={0730-0301}, journal={ACM Transactions on Graphics}, author={Liu, Yanchao and Guo, Jianwei and Benes, Bedrich and Deussen, Oliver and Zhang, Xiaopeng and Huang, Hui}, note={Article Number: 232} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55977"> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-22T08:07:58Z</dcterms:available> <dc:creator>Guo, Jianwei</dc:creator> <dc:creator>Deussen, Oliver</dc:creator> <dc:creator>Liu, Yanchao</dc:creator> <dc:creator>Zhang, Xiaopeng</dc:creator> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55977/1/Liu_2-13rmq34uz5o9h2.pdf"/> <dcterms:issued>2021</dcterms:issued> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55977/1/Liu_2-13rmq34uz5o9h2.pdf"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55977"/> <dc:contributor>Zhang, Xiaopeng</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-22T08:07:58Z</dc:date> <dc:contributor>Huang, Hui</dc:contributor> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:title>TreePartNet : neural decomposition of point clouds for 3D tree reconstruction</dcterms:title> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:rights>terms-of-use</dc:rights> <dc:creator>Huang, Hui</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:contributor>Guo, Jianwei</dc:contributor> <dc:contributor>Liu, Yanchao</dc:contributor> <dc:contributor>Benes, Bedrich</dc:contributor> <dcterms:abstract xml:lang="eng">We present TreePartNet, a neural network aimed at reconstructing tree geometry from point clouds obtained by scanning real trees. Our key idea is to learn a natural neural decomposition exploiting the assumption that a tree comprises locally cylindrical shapes. In particular, reconstruction is a two-step process. First, two networks are used to detect priors from the point clouds. One detects semantic branching points, and the other network is trained to learn a cylindrical representation of the branches. In the second step, we apply a neural merging module to reduce the cylindrical representation to a final set of generalized cylinders combined by branches. We demonstrate results of reconstructing realistic tree geometry for a variety of input models and with varying input point quality, e.g., noise, outliers, and incompleteness. We evaluate our approach extensively by using data from both synthetic and real trees and comparing it with alternative methods.</dcterms:abstract> <dc:creator>Benes, Bedrich</dc:creator> </rdf:Description> </rdf:RDF>