Evaluating Generative AI for Museum Artifacts Documentation

Abstract. In recent years, the European Commission (EC) identified the 3D digitization of cultural heritage sites and artifacts as one of its priorities and promoted numerous initiatives and recommendations to accelerate documentation campaigns. However, current digitization targets remain far from being achieved, and heritage institutions have been increasingly encouraged to explore faster and cost-effective 3D documentation solutions. Moreover, traditional image- and range-based 3D surveying techniques frequently struggle when reconstructing objects featuring non-collaborative surfaces (such as reflective or transparent objects), are time-consuming, and require specialized knowledge. Generative AI methods, able to generate 3D models also from a single input image, have recently emerged as a potentially faster alternative, yet their performance on heritage assets remains mostly unexplored. This paper evaluates three state-of-the-art and recent single-image GenAI frameworks - SAM3D, Tripo3D and Trellis2 - on several museum artifacts featuring diffuse, reflective, transparent, and mixed-material surfaces of varying scale and geometric complexity, for which accurate ground truth is available. The aim is to analyze whether these frameworks can act as complementary or alternative solutions for fast heritage documentation.