arXiv:2405.06143v1 Announce Type: cross
Abstract: Recent years have witnessed many advancements in the applications of 3D textured meshes. As the demand continues to rise, evaluating the perceptual quality of this new type of media content becomes crucial for quality assurance and optimization purposes. Different from traditional image quality assessment, crack is an annoying artifact specific to rendered 3D meshes that severely affects their perceptual quality. In this work, we make one of the first attempts to propose a novel Perceptual Crack Detection (PCD) method for detecting and localizing crack artifacts in rendered meshes. Specifically, motivated by the characteristics of the human visual system (HVS), we adopt contrast and Laplacian measurement modules to characterize crack artifacts and differentiate them from other undesired artifacts. Extensive experiments on large-scale public datasets of 3D textured meshes demonstrate effectiveness and efficiency of the proposed PCD method in correct localization and detection of crack artifacts. %Specifically, We propose a full-reference crack artifact localization method that operates on a pair of input snapshots of distorted and reference 3D objects to generate a final crack map. Moreover, to quantify the performance of the proposed detection method and validate its effectiveness, we propose a simple yet effective weighting mechanism to incorporate the resulting crack map into classical quality assessment (QA) models, which creates significant performance improvement in predicting the perceptual image quality when tested on public datasets of static 3D textured meshes. A software release of the proposed method is publicly available at: https://github.com/arshafiee/crack-detection-VVM
The Importance of Perceptual Crack Detection in 3D Textured Meshes
Advancements in the field of 3D textured meshes have been rapidly increasing, leading to a rise in demand for evaluating the perceptual quality of this type of media content. Ensuring the quality of rendered 3D meshes is crucial for various applications, including virtual reality, gaming, and architectural design. One particular artifact that significantly affects the perceptual quality of these meshes is cracks.
Cracks in rendered 3D meshes are annoying visual artifacts that can distort the overall appearance and realism of the content. Therefore, accurately detecting and localizing these crack artifacts is essential for quality assurance and optimization purposes.
The novel Perceptual Crack Detection (PCD) method proposed in this work aims to address this issue. The authors take inspiration from the human visual system (HVS) and adopt contrast and Laplacian measurement modules to characterize crack artifacts and distinguish them from other undesired artifacts. This approach leverages the unique characteristics of the HVS to improve the accuracy of crack detection and localization.
Extensive experiments on large-scale public datasets of 3D textured meshes have been conducted to evaluate the effectiveness and efficiency of the proposed PCD method. The results demonstrate that the method successfully localizes and detects crack artifacts, showcasing its potential for integration into quality assessment (QA) models. The authors also propose a weighting mechanism that incorporates the crack map generated by the PCD method into classical QA models, leading to a significant improvement in predicting the perceptual image quality.
This research highlights the multi-disciplinary nature of the concepts involved. It combines knowledge from computer graphics, human visual perception, and image quality assessment to tackle the specific challenge of crack detection in rendered 3D meshes. The proposed method provides a valuable tool for industry professionals and researchers working in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
In conclusion, the introduction of the Perceptual Crack Detection (PCD) method addresses a critical issue in the evaluation of 3D textured meshes. By leveraging the characteristics of the human visual system, this approach effectively detects and localizes crack artifacts, enhancing the overall perceptual quality of rendered 3D meshes. The multi-disciplinary nature of the research makes it relevant to the wider field of multimedia information systems and various applications involving animations, artificial reality, augmented reality, and virtual realities.