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:
this https URL

该研究提出了一种用于检测和定位渲染网格中裂缝伪影的新型感知性裂缝检测方法，并在大规模公共数据集上进行了实验证明其有效性和效率。