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Communication Dans Un Congrès Année : 2023

Multi-stream POINTNet-based Model For Blind Geometric Point Cloud Quality Assessment

Résumé

The evaluation of 3D point cloud quality is a critical com- ponent in the development of immersive multimedia systems for real-world applications. While perceptual quality evalua- tion techniques for 2D images and videos have reached high performances, developing robust and efficient blind metrics for point cloud quality assessment is still challenging. In this paper, we propose a no-reference point cloud quality as- sessment method that evaluates the quality of degraded 3D objects using an end-to-end multi-stream point-based model. We introduce geometric coordinates, normals, and curvatures as inputs to the proposed model stream to extract significant degradation features that are incorporated to predict the vi- sual quality of 3D point clouds. Experimental results on two benchmark databases: ICIP202 and SJTU, demonstrate that the proposed model achieves promising performance com- pared to state-of-the-art full and reduced methods.
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Dates et versions

hal-04125265 , version 1 (12-06-2023)

Identifiants

  • HAL Id : hal-04125265 , version 1

Citer

Salima Bourbia, Ayoub Karine, Aladine Chetouani, Mohammed El Hassouni, Maher Jridi. Multi-stream POINTNet-based Model For Blind Geometric Point Cloud Quality Assessment. 20th International Conference on Content-based Multimedia Indexing, Sep 2023, Orléans, France. ⟨hal-04125265⟩
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