用单图生成高精度3D艺术模型，助力博物馆沉浸式互动体验
Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction
- 融合GLPN与Depth-Anything双模型，优化深度图生成
- 重建精度与视觉真实感显著提升，支持复杂纹理与轮廓
- 适合文博机构构建交互式数字展览内容
本文提出一种面向博物馆场景的增强现实流程，旨在通过单张图像识别艺术品并生成精确的3D模型。该方法整合两种互补的预训练深度估计模型：GLPN用于捕捉全局场景结构，Depth-Anything用于实现细节丰富的局部重建，从而生成优化的深度图。这些深度图被转换为高质量点云与网格，支持沉浸式AR体验的构建。该方法利用最先进的神经网络架构和计算机视觉技术，有效应对艺术品中不规则轮廓与多变纹理带来的挑战。实验结果表明，重建精度与视觉真实感均有显著提升，使系统成为博物馆提升观众互动参与度的强大工具。
原文摘要 · Abstract (English)
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.
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