On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models.

3D meshes are deployed in a wide range of application processes (e.g., transmission, compression, simplification, watermarking and so on) which inevitably introduce geometric distortions that may alter the visual quality of the rendered data. Hence, efficient model-based perceptual metrics, operating on the geometry of the meshes being compared, have been recently introduced to control and predict these visual artifacts. However, since the 3D models are ultimately visualized on 2D screens, it seems legitimate to use images of the models (i.e., snapshots from different viewpoints) to evaluate their visual fidelity. In this work we investigate the use of image metrics to assess the visual quality of 3D models. For this goal, we conduct a wide-ranging study involving several 2D metrics, rendering algorithms, lighting conditions and pooling algorithms, as well as several mean opinion score databases. The collected data allow (1) to determine the best set of parameters to use for this image-based quality assessment approach and (2) to compare this approach to the best performing model-based metrics and determine for which use-case they are respectively adapted. We conclude by exploring several applications that illustrate the benefits of image-based quality assessment.