Abstract
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.
Abstract (translated)
本文提出了一种创新的增强现实（AR）处理流程，专门针对博物馆环境设计，旨在识别艺术品并从单张图像中生成准确的3D模型。通过结合两个互补的预训练深度估计模型——GLPN 用于捕捉全局场景结构和Depth-Anything 用于详细局部重建——所提出的方案能够产生优化的深度图，这些图表有效地代表了复杂的艺术特征。随后，这些地图被转换为高质量的点云和网格，从而可以创建沉浸式的AR体验。 该方法利用最先进的神经网络架构和高级计算机视觉技术来克服由艺术品不规则轮廓和可变纹理带来的挑战。实验结果表明，在重建精度和视觉真实感方面取得了显著改善，使系统成为博物馆增强访客互动体验通过数字内容所需的强大工具。
URL
https://arxiv.org/abs/2507.13719
