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cs.CV2026

The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments

Ofir Itzhak Shahar, Gur Elkin, Ohad Ben-Shahar

Jigsaw puzzle solving has been an increasingly popular task in the computer vision research community. Recent works have utilized cutting-edge architectures and computational appro…

cs.CV2025

Solving Convex Partition Visual Jigsaw Puzzles

Yaniv Ohayon, Ofir Itzhak Shahar, Ohad Ben-Shahar

Jigsaw puzzle solving requires the rearrangement of unordered pieces into their original pose in order to reconstruct a coherent whole, often an image, and is known to be an intrac…

cs.CV2025

PuzLM: Solving Jigsaw Puzzles with Sequence-to-Sequence Language Models

Gur Elkin, Ofir Itzhak Shahar, Ohad Ben-Shahar

Square jigsaw puzzles are typically solved by visually matching piece images to recover the original layout. This work introduces PuzLM, an alternative perspective that recasts jig…

cs.CV2025

Pairwise Alignment & Compatibility for Arbitrarily Irregular Image Fragments

Ofir Itzhak Shahar, Gur Elkin, Ohad Ben-Shahar

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. H…

cs.CV2025

Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation

Gur Elkin, Ofir Itzhak Shahar, Yaniv Ohayon +2

Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from…

cs.CV2024

Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving

Theodore Tsesmelis, Luca Palmieri, Marina Khoroshiltseva +20

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dat…