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arXiv · 2501.19325

A Generic Hybrid Framework for 2D Visual Reconstruction

Daniel Rika·Dror Sholomon·Eli David·Alexandre Pais·Nathan S. Netanyahu
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Abstract

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. Our approach integrates a deep learning (DL)-based compatibility measure (CM) model that evaluates pairs of puzzle pieces holistically, rather than focusing solely on their adjacent edges as traditionally done. This DL-based CM is paired with an optimized genetic algorithm (GA)-based solver, which iteratively searches for a global optimal arrangement using the pairwise CM scores of the puzzle pieces. Extensive experimental results highlight the framework's adaptability and robustness across multiple real-world domains. Notably, our unique hybrid methodology achieves state-of-the-art (SOTA) results in reconstructing Portuguese tile panels and large degraded puzzles with eroded boundaries.

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Daniel Rika, Dror Sholomon, Eli David, Alexandre Pais, Nathan S. Netanyahu. 2025-01-31. A Generic Hybrid Framework for 2D Visual Reconstruction. https://arxiv.org/abs/2501.19325

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