Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction

• URL: http://arxiv.org/abs/2507.13719v1
• Date: Fri, 18 Jul 2025 07:59:29 GMT
• Title: Augmented Reality in Cultural Heritage: A Dual-Model Pipeline for 3D Artwork Reconstruction
• Authors: Daniele Pannone, Alessia Castronovo, Maurizio Mancini, Gian Luca Foresti, Claudio Piciarelli, Rossana Gabrieli, Muhammad Yasir Bilal, Danilo Avola,
• Abstract summary: This paper presents an innovative augmented reality pipeline tailored for museum environments.<n>It is aimed at recognizing artworks and generating accurate 3D models from single images.
• Score: 12.45946835655036
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• High-fidelity 3D Gaussian Inpainting: preserving multi-view consistency and photorealistic details [8.279171283542066]
  Inpainting 3D scenes remains a challenging task due to the inherent irregularity of 3D structures.<n>We propose a novel 3D Gaussian inpainting framework that reconstructs complete 3D scenes by leveraging sparse inpainted views.<n>Our approach outperforms existing state-of-the-art methods in both visual quality and view consistency.
  arXiv  Detail & Related papers  (2025-07-24T01:48:50Z)
• Advances in Feed-Forward 3D Reconstruction and View Synthesis: A Survey [154.50661618628433]
  3D reconstruction and view synthesis are foundational problems in computer vision, graphics, and immersive technologies such as augmented reality (AR), virtual reality (VR), and digital twins.<n>Recent advances in feed-forward approaches, driven by deep learning, have revolutionized this field by enabling fast and generalizable 3D reconstruction and view synthesis.
  arXiv  Detail & Related papers  (2025-07-19T06:13:25Z)
• DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos [52.46386528202226]
  We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM)<n>It is the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed video of any dynamic scene.<n>It achieves performance on par with state-of-the-art monocular video 3D tracking methods.
  arXiv  Detail & Related papers  (2025-06-11T17:59:58Z)
• A Generative Approach to High Fidelity 3D Reconstruction from Text Data [0.0]
  This research proposes a fully automated pipeline that seamlessly integrates text-to-image generation, various image processing techniques, and deep learning methods for reflection removal and 3D reconstruction.<n>By leveraging state-of-the-art generative models like Stable Diffusion, the methodology translates natural language inputs into detailed 3D models through a multi-stage workflow.<n>This approach addresses key challenges in generative reconstruction, such as maintaining semantic coherence, managing geometric complexity, and preserving detailed visual information.
  arXiv  Detail & Related papers  (2025-03-05T16:54:15Z)
• Textured Mesh Saliency: Bridging Geometry and Texture for Human Perception in 3D Graphics [50.23625950905638]
  We present a new dataset for textured mesh saliency, created through an innovative eye-tracking experiment in a six degrees of freedom (6-DOF) VR environment.<n>Our proposed model predicts saliency maps for textured mesh surfaces by treating each triangular face as an individual unit and assigning a saliency density value to reflect the importance of each local surface region.
  arXiv  Detail & Related papers  (2024-12-11T08:27:33Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• M3D: Dual-Stream Selective State Spaces and Depth-Driven Framework for High-Fidelity Single-View 3D Reconstruction [3.2228041579285978]
  M3D is a novel single-view 3D reconstruction framework for complex scenes. It balances the extraction of global and local features, thereby improving scene comprehension and representation precision. Results indicate that the fusion of multi-scale features with depth information via the dual-branch feature extraction significantly boosts geometric consistency and fidelity.
  arXiv  Detail & Related papers  (2024-11-19T16:49:24Z)
• Enhancement of 3D Gaussian Splatting using Raw Mesh for Photorealistic Recreation of Architectures [12.96911281844627]
  We propose a method to harness raw 3D models to guide 3D Gaussians in capturing the basic shape of a building. This exploration opens up new possibilities for improving the effectiveness of 3D reconstruction techniques in the field of architectural design.
  arXiv  Detail & Related papers  (2024-07-22T07:29:38Z)
• Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs [57.492124844326206]
  This work delves into the task of pose-free novel view synthesis from stereo pairs, a challenging and pioneering task in 3D vision. Our innovative framework, unlike any before, seamlessly integrates 2D correspondence matching, camera pose estimation, and NeRF rendering, fostering a synergistic enhancement of these tasks.
  arXiv  Detail & Related papers  (2023-12-12T13:22:44Z)
• HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via High-Fidelity Texture [40.012406098563204]
  We present HiFiHR, a high-fidelity hand reconstruction approach that utilizes render-and-compare in the learning-based framework from a single image. Experimental results on public benchmarks including FreiHAND and HO-3D demonstrate that our method outperforms the state-of-the-art hand reconstruction methods in texture reconstruction quality.
  arXiv  Detail & Related papers  (2023-08-25T18:48:40Z)
• Towards Realistic 3D Embedding via View Alignment [53.89445873577063]
  This paper presents an innovative View Alignment GAN (VA-GAN) that composes new images by embedding 3D models into 2D background images realistically and automatically. VA-GAN consists of a texture generator and a differential discriminator that are inter-connected and end-to-end trainable.
  arXiv  Detail & Related papers  (2020-07-14T14:45:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.