Alignment of 3D woodblock geometrical models and 2D orthographic projection image

• URL: http://arxiv.org/abs/2411.05524v1
• Date: Fri, 08 Nov 2024 12:30:41 GMT
• Title: Alignment of 3D woodblock geometrical models and 2D orthographic projection image
• Authors: Minh DUc Nguyen, Cong Thuong Le, Trong Lam Nguyen,
• Abstract summary: This paper proposes a unified image processing algorithm to address this issue. The method includes determining the plane of the 3D character model, establishing a transformation matrix, and creating a parallel-projected depth map. Experimental results highlight the importance of structure-based comparisons to optimize alignment for large-scale Han-Nom character datasets.
• Score: 0.0
• License:
• Abstract: The accurate alignment of 3D woodblock geometrical models with 2D orthographic projection images presents a significant challenge in the digital preservation of Vietnamese cultural heritage. This paper proposes a unified image processing algorithm to address this issue, enhancing the registration quality between 3D woodblock models and their 2D representations. The method includes determining the plane of the 3D character model, establishing a transformation matrix to align this plane with the 2D printed image plane, and creating a parallel-projected depth map for precise alignment. This process minimizes disocclusions and ensures that character shapes and strokes are correctly positioned. Experimental results highlight the importance of structure-based comparisons to optimize alignment for large-scale Han-Nom character datasets. The proposed approach, combining density-based and structure-based methods, demonstrates improved registration performance, offering an effective normalization scheme for digital heritage preservation.

Related papers

• BIFRÖST: 3D-Aware Image compositing with Language Instructions [27.484947109237964]
  Bifr"ost is a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition. Bifr"ost addresses issues by training MLLM as a 2.5D location predictor and integrating depth maps as an extra condition during the generation process.
  arXiv  Detail & Related papers  (2024-10-24T18:35:12Z)
• VCD-Texture: Variance Alignment based 3D-2D Co-Denoising for Text-Guided Texturing [22.39760469467524]
  We propose a Variance texture synthesis to address the modal gap between the 2D and 3D diffusion models. We present an inpainting module to improve details with conflicting regions.
  arXiv  Detail & Related papers  (2024-07-05T12:11:33Z)
• LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image [64.94932577552458]
  Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images. Despite their success, these models often produce 3D meshes with geometric inaccuracies, stemming from the inherent challenges of deducing 3D shapes solely from image data. We introduce a novel framework, the Large Image and Point Cloud Alignment Model (LAM3D), which utilizes 3D point cloud data to enhance the fidelity of generated 3D meshes.
  arXiv  Detail & Related papers  (2024-05-24T15:09:12Z)
• GeoGS3D: Single-view 3D Reconstruction via Geometric-aware Diffusion Model and Gaussian Splatting [81.03553265684184]
  We introduce GeoGS3D, a framework for reconstructing detailed 3D objects from single-view images. We propose a novel metric, Gaussian Divergence Significance (GDS), to prune unnecessary operations during optimization. Experiments demonstrate that GeoGS3D generates images with high consistency across views and reconstructs high-quality 3D objects.
  arXiv  Detail & Related papers  (2024-03-15T12:24:36Z)
• Wonder3D: Single Image to 3D using Cross-Domain Diffusion [105.16622018766236]
  Wonder3D is a novel method for efficiently generating high-fidelity textured meshes from single-view images. To holistically improve the quality, consistency, and efficiency of image-to-3D tasks, we propose a cross-domain diffusion model.
  arXiv  Detail & Related papers  (2023-10-23T15:02:23Z)
• Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image [8.368476827165114]
  This paper proposes a framework to recover the 3D shape of 2D landmarks on a human face, in a single input image. A deep neural network learns the pairwise dissimilarity among 2D landmarks, used by NMDS approach.
  arXiv  Detail & Related papers  (2022-10-27T06:20:10Z)
• RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects [68.85305626324694]
  Ray-marching in Camera Space (RiCS) is a new method to represent the self-occlusions of foreground objects in 3D into a 2D self-occlusion map. We show that our representation map not only allows us to enhance the image quality but also to model temporally coherent complex shadow effects.
  arXiv  Detail & Related papers  (2022-05-14T05:35:35Z)
• Translational Symmetry-Aware Facade Parsing for 3D Building Reconstruction [11.263458202880038]
  In this paper, we present a novel translational symmetry-based approach to improving the deep neural networks. We propose a novel scheme to fuse anchor-free detection in a single stage network, which enables the efficient training and better convergence. We employ an off-the-shelf rendering engine like Blender to reconstruct the realistic high-quality 3D models using procedural modeling.
  arXiv  Detail & Related papers  (2021-06-02T03:10:51Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• 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)
• Adaptive 3D Face Reconstruction from a Single Image [45.736818498242016]
  We propose a novel joint 2D and 3D optimization method to adaptively reconstruct 3D face shapes from a single image. Experimental results on multiple datasets demonstrate that our method can generate high-quality reconstruction from a single color image.
  arXiv  Detail & Related papers  (2020-07-08T09:35:26Z)

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.