Pairwise Alignment & Compatibility for Arbitrarily Irregular Image Fragments
- URL: http://arxiv.org/abs/2507.09767v1
- Date: Sun, 13 Jul 2025 19:49:42 GMT
- Title: Pairwise Alignment & Compatibility for Arbitrarily Irregular Image Fragments
- Authors: Ofir Itzhak Shahar, Gur Elkin, Ohad Ben-Shahar,
- Abstract summary: We propose an efficient hybrid (geometric and pictorial) approach for computing the optimal alignment for pairs of fragments.<n>We then embed our proposed compatibility into an archaeological puzzle-solving framework and demonstrate state-of-the-art neighborhood-level precision.
- Score: 3.1918203325276564
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pairwise compatibility calculation is at the core of most fragments-reconstruction algorithms, in particular those designed to solve different types of the jigsaw puzzle problem. However, most existing approaches fail, or aren't designed to deal with fragments of realistic geometric properties one encounters in real-life puzzles. And in all other cases, compatibility methods rely strongly on the restricted shapes of the fragments. In this paper, we propose an efficient hybrid (geometric and pictorial) approach for computing the optimal alignment for pairs of fragments, without any assumptions about their shapes, dimensions, or pictorial content. We introduce a new image fragments dataset generated via a novel method for image fragmentation and a formal erosion model that mimics real-world archaeological erosion, along with evaluation metrics for the compatibility task. We then embed our proposed compatibility into an archaeological puzzle-solving framework and demonstrate state-of-the-art neighborhood-level precision and recall on the RePAIR 2D dataset, directly reflecting compatibility performance improvements.
Related papers
- Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image [52.11275397911693]
We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image.<n>We depart from previous works that rely on learning instance-level latent space, focusing on man-made articulated objects with predefined part counts.<n>Our method successfully reconstructs variously structured multiple instances that previous works cannot handle, and outperforms prior works in shape reconstruction and kinematics estimation.
arXiv Detail & Related papers (2025-04-04T05:08:04Z) - A Generic Hybrid Framework for 2D Visual Reconstruction [39.58317527488534]
This paper presents a versatile hybrid framework for addressing 2D real-world reconstruction tasks formulated as jigsaw puzzle problems (JPPs) with square, non-overlapping pieces.<n>Our approach integrates a deep learning (DL)-based compatibility measure (CM) model that evaluates pairs of puzzle pieces holistically.<n>Our unique hybrid methodology achieves state-of-the-art (SOTA) results in reconstructing Portuguese tile panels and large degraded puzzles with eroded boundaries.
arXiv Detail & Related papers (2025-01-31T17:21:29Z) - Image Matching Filtering and Refinement by Planes and Beyond [8.184339776177486]
This paper introduces a modular, non-deep learning method for filtering and refining sparse correspondences in image matching.<n>The proposed method is extensively evaluated on standard datasets and image matching pipelines, and compared with state-of-the-art approaches.<n> Experimental results demonstrate that our proposed non-deep learning, geometry-based approach achieves performances that are either superior to or on par with recent state-of-the-art deep learning methods.
arXiv Detail & Related papers (2024-11-14T14:37:50Z) - Scalable Geometric Fracture Assembly via Co-creation Space among
Assemblers [24.89380678499307]
We develop a scalable framework for geometric fracture assembly without relying on semantic information.
We introduce a novel loss function, i.e., the geometric-based collision loss, to address collision issues during the fracture assembly process.
Our framework exhibits better performance on both PartNet and Breaking Bad datasets compared to existing state-of-the-art frameworks.
arXiv Detail & Related papers (2023-12-19T17:13:51Z) - PairingNet: A Learning-based Pair-searching and -matching Network for Image Fragments [6.317537547004322]
We propose a learning-based image fragment pair-searching and -matching approach to solve the challenging restoration problem.<n>Our proposed network achieves excellent pair-searching accuracy, reduces matching errors, and significantly reduces computational time.
arXiv Detail & Related papers (2023-12-14T07:43:53Z) - COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
Face forgery recognition methods can only process one face at a time.
Most face forgery recognition methods can only process one face at a time.
We propose COMICS, an end-to-end framework for multi-face forgery detection.
arXiv Detail & Related papers (2023-08-03T03:37:13Z) - Parallax-Tolerant Unsupervised Deep Image Stitching [57.76737888499145]
We propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique.
First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion.
To further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks.
arXiv Detail & Related papers (2023-02-16T10:40:55Z) - Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
We propose a multi-level contrastive learning approach for semantic matching.
We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
arXiv Detail & Related papers (2021-09-22T18:34:14Z) - Isometric Multi-Shape Matching [50.86135294068138]
Finding correspondences between shapes is a fundamental problem in computer vision and graphics.
While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting.
We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis.
arXiv Detail & Related papers (2020-12-04T15:58:34Z) - Non-Rigid Puzzles [50.213265511586535]
We present a non-rigid multi-part shape matching algorithm.
We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation.
Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method.
arXiv Detail & Related papers (2020-11-26T00:32:30Z)
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.