A toolkit to benchmark point cloud quality metrics with multi-track evaluation criteria - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

A toolkit to benchmark point cloud quality metrics with multi-track evaluation criteria

Résumé

Point clouds (PCs) gained popularity as a representation for 3D objects and scenes and are widely used in numerous applications in augmented and virtual reality domains. Concurrently, quality assessment of PCs became even more relevant to improve various aspects of these imaging pipelines. To stimulate further growth and interest in point cloud quality assessment (PCQA), we created a large-scale PCQA dataset (called “BASICS”) which provides the research community with a relevant and challenging dataset to develop reliable objective quality metrics, and we organized the PCVQA grand challenge at ICIP 2023. In this paper, we provide a track-based evaluation methodology for benchmarking visual quality metrics, mirroring the PCVQA grand challenge evaluation scenarios designed to mimic real-life applications. Furthermore, we provide a state-of-theart benchmark for the point cloud quality metrics. The track-based benchmarking approach shows that there is room for improvement in certain research directions, drawing attention to open problems in the PCQA domain.
Fichier principal
Vignette du fichier
ICIP2024_submission.pdf (1.12 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04615285 , version 1 (18-06-2024)

Identifiants

  • HAL Id : hal-04615285 , version 1

Citer

Ali Ak, Emin Zerman, Maurice Quach, Aladine Chetouani, Giuseppe Valenzise, et al.. A toolkit to benchmark point cloud quality metrics with multi-track evaluation criteria. 2024 IEEE International Conference on Image Processing (ICIP 2024), IEEE, Oct 2024, ABU DHABI, United Arab Emirates. ⟨hal-04615285⟩
0 Consultations
0 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More