From pictorial space to tactile form: a comparative evaluation of AI-based 2.5D reconstruction from modern artwork paintings

Introduction The translation of paintings into tactile 2.5D models (i.e., bas-reliefs) represents a significant advancement in improving accessibility for blind and visually impaired individuals. However, reconstructing spatial structure from a single painted image without explicit perspective is inherently ill-posed, particularly in modern and contemporary artworks where perspective, illumination, and geometry deviate from physical realism. Methods This study presents a comparative evaluation of three AI-based reconstruction paradigms: Monocular Depth Estimation, Large Language Models, and Large Reconstruction Models. These approaches are applied to a selected corpus of photographic, realist, and abstract artworks from the CSAC collection (Parma, Italy). An assessment framework is introduced, combining expert-based qualitative evaluation by art historians, formal geometric verification (including integrability and topological consistency), and manufacturability analysis conducted by additive manufacturing specialists. Results The results indicate that Large Language Model-based methods generate semantically rich and perceptually plausible bas-reliefs but lack geometric integrability and topological robustness. Monocular Depth Models perform well in capturing depth hierarchies but tend to oversmooth fine details. Large Reconstruction Models demonstrate strong structural coherence and fabrication readiness, though they often struggle with stylistic reinterpretation. Discussion These findings highlight the trade-offs among current AI-based reconstruction approaches for tactile bas-relief generation. While each paradigm excels in specific aspects, none achieves a complete balance between perceptual fidelity, geometric soundness, and manufacturability. Future work should focus on hybrid strategies that integrate semantic understanding with geometric consistency to better support accessible cultural heritage applications.