Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis

Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that...

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Detalles Bibliográficos
Autores: Bravo Pérez-Villar, Juan Ignacio, García Martín, Álvaro, Bescos Cano, Jesús, San Miguel Avedillo, Juan Carlos
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/715632
Acceso en línea:http://hdl.handle.net/10486/715632
https://dx.doi.org/10.1109/TAES.2024.3410956
Access Level:acceso abierto
Palabra clave:Adaptation models
Feature extraction
Keypoint
pose estimation
Pose estimation
Space heating
Space vehicles
Task analysis
test time adaptation
Training
view synthesis
Telecomunicaciones
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spelling Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesisBravo Pérez-Villar, Juan IgnacioGarcía Martín, ÁlvaroBescos Cano, JesúsSan Miguel Avedillo, Juan CarlosAdaptation modelsFeature extractionKeypointpose estimationPose estimationSpace heatingSpace vehiclesTask analysistest time adaptationTrainingview synthesisTelecomunicacionesDue to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesise a reconstructed view. We establish a self-supervised learning objective by comparing the synthesised view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test-time, we optimise the self-supervised objective. Additionally, we introduce a regularisation loss to prevent solutions that are not consistent with the keypoint structure of the spacecraftThis work is supported by Comunidad Autonoma de Madrid (Spain) under the Grant IND2020/TIC-17515 and the HVD (PID2021-125051OB-I00) project funded by the Ministerio de Ciencia e Innovación of the Spanish GovernmentIEEEDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica SuperiorVideo Processing and Understanding Labgrupo de Tratamiento e Interpretación de Vídeo20242024-06-07research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/715632https://dx.doi.org/10.1109/TAES.2024.3410956reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7156322026-06-23T12:46:27Z
dc.title.none.fl_str_mv Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
title Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
spellingShingle Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
Bravo Pérez-Villar, Juan Ignacio
Adaptation models
Feature extraction
Keypoint
pose estimation
Pose estimation
Space heating
Space vehicles
Task analysis
test time adaptation
Training
view synthesis
Telecomunicaciones
title_short Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
title_full Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
title_fullStr Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
title_full_unstemmed Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
title_sort Test-time adaptation for keypoint-based spacecraft pose estimation based on predicted-view synthesis
dc.creator.none.fl_str_mv Bravo Pérez-Villar, Juan Ignacio
García Martín, Álvaro
Bescos Cano, Jesús
San Miguel Avedillo, Juan Carlos
author Bravo Pérez-Villar, Juan Ignacio
author_facet Bravo Pérez-Villar, Juan Ignacio
García Martín, Álvaro
Bescos Cano, Jesús
San Miguel Avedillo, Juan Carlos
author_role author
author2 García Martín, Álvaro
Bescos Cano, Jesús
San Miguel Avedillo, Juan Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
Video Processing and Understanding Labgrupo de Tratamiento e Interpretación de Vídeo
dc.subject.none.fl_str_mv Adaptation models
Feature extraction
Keypoint
pose estimation
Pose estimation
Space heating
Space vehicles
Task analysis
test time adaptation
Training
view synthesis
Telecomunicaciones
topic Adaptation models
Feature extraction
Keypoint
pose estimation
Pose estimation
Space heating
Space vehicles
Task analysis
test time adaptation
Training
view synthesis
Telecomunicaciones
description Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesise a reconstructed view. We establish a self-supervised learning objective by comparing the synthesised view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test-time, we optimise the self-supervised objective. Additionally, we introduce a regularisation loss to prevent solutions that are not consistent with the keypoint structure of the spacecraft
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-06-07
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/715632
https://dx.doi.org/10.1109/TAES.2024.3410956
url http://hdl.handle.net/10486/715632
https://dx.doi.org/10.1109/TAES.2024.3410956
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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