Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction
It addresses the need for immersive digital experiences in cultural heritage settings, offering a robust tool for museums to enhance visitor engagement.
This paper tackles the problem of reconstructing 3D models of artworks from single images for augmented reality in museums, achieving significant improvements in accuracy and realism through a dual-model pipeline.
This paper presents an innovative augmented reality pipeline tailored for museum environments, aimed at recognizing artworks and generating accurate 3D models from single images. By integrating two complementary pre-trained depth estimation models, i.e., GLPN for capturing global scene structure and Depth-Anything for detailed local reconstruction, the proposed approach produces optimized depth maps that effectively represent complex artistic features. These maps are then converted into high-quality point clouds and meshes, enabling the creation of immersive AR experiences. The methodology leverages state-of-the-art neural network architectures and advanced computer vision techniques to overcome challenges posed by irregular contours and variable textures in artworks. Experimental results demonstrate significant improvements in reconstruction accuracy and visual realism, making the system a highly robust tool for museums seeking to enhance visitor engagement through interactive digital content.