LightDepth: Single-view depth self-supervision from illumination decline

Rodríguez-Puigvert, Javier (Universidad de Zaragoza) ; Batlle, Víctor M. (Universidad de Zaragoza) ; Martínez Montiel, J.M. (Universidad de Zaragoza) ; Martinez-Cantin, Ruben (Universidad de Zaragoza) ; Fua, Pascal ; Tardós, Juan D. (Universidad de Zaragoza) ; Civera, Javier (Universidad de Zaragoza)

Resumen: Single-view depth estimation can be remarkably effective if there is enough ground-truth depth data for supervised training. However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained. In such cases, multi-view self-supervision and synthetic-to-real transfer serve as alternative approaches, however, with a considerable performance reduction in comparison to supervised case. Instead, we propose a single-view self-supervised method that achieves a performance similar to the supervised case. In some medical devices, such as endoscopes, the camera and light sources are co-located at a small distance from the target surfaces. Thus, we can exploit that, for any given albedo and surface orientation, pixel brightness is inversely proportional to the square of the distance to the surface, providing a strong single-view self-supervisory signal. In our experiments, our self-supervised models deliver accuracies comparable to those of fully supervised ones, while being applicable without depth ground-truth data.

Idioma: Inglés

DOI: 10.1109/ICCV51070.2023.01945

Año: 2024

Publicado en: Proceedings (IEEE International Conference on Computer Vision) 2023 (2024), 21216-21226
ISSN: 1550-5499

Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-23R
Financiación: info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-096367-B-I00
Financiación: info:eu-repo/grantAgreement/ES/MCIU/FPU20-0678
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/PID2021-127685NB-I00
Financiación: info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131150B-I00

Tipo y forma: Comunicación congreso (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)