Quality assessment of 3D reconstructed meshes: Bridging objective metrics, subjective perception, and behavioral cues

Assessing the quality of 3D reconstructed models remains a key challenge in multimedia applications, especially in the context of cultural heritage, where visual fidelity and perceptual realism are equally crucial. This study investigates how reconstruction parameters, as well as existing objective quality metrics, align with human perception. In addition, we analyze how perceived quality and user interaction are related. A dataset of 3D models was generated by varying the number of input images, mesh complexity, and texture resolution. Results from a subjective study show that texture resolution significantly affects perceived quality, whereas variations in number of images and mesh complexity have a limited impact. Furthermore, interaction behavior was found to vary with perceived quality, with participants spending more time and exploring larger viewing angles for models receiving higher scores. These findings highlight the need for perceptually grounded, interaction-aware evaluation methodologies and provide guidelines for future perceptual optimization of 3D reconstruction pipelines. • We present a dataset of 3D models with varying reconstruction parameters. The dataset includes the 3D meshes, objective metrics, subjective ratings from 41 participants, and interaction logs to support future perceptual modeling studies. The dataset will be made publicly available (upon publication). • Image quality assessment metrics were evaluated in terms of their alignment with human perception. Full reference metrics proved to be a valuable benchmark for the evaluation of novel no reference metrics. • Subjective results show higher mean opinion scores with increased texture detail, while the number of images used to reconstruct the mesh and its geometrical structure had limited perceptual impact. • The results highlighted links between perceived quality and user behavior.