PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving

• URL: http://arxiv.org/abs/2211.13785v3
• Date: Tue, 3 Oct 2023 22:29:43 GMT
• Title: PuzzleFusion: Unleashing the Power of Diffusion Models for Spatial Puzzle Solving
• Authors: Sepidehsadat Hosseini, Mohammad Amin Shabani, Saghar Irandoust, Yasutaka Furukawa
• Abstract summary: The paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving. A surprising discovery is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements.
• Score: 17.781484376483707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents an end-to-end neural architecture based on Diffusion Models for spatial puzzle solving, particularly jigsaw puzzle and room arrangement tasks. In the latter task, for instance, the proposed system "PuzzleFusion" takes a set of room layouts as polygonal curves in the top-down view and aligns the room layout pieces by estimating their 2D translations and rotations, akin to solving the jigsaw puzzle of room layouts. A surprising discovery of the paper is that the simple use of a Diffusion Model effectively solves these challenging spatial puzzle tasks as a conditional generation process. To enable learning of an end-to-end neural system, the paper introduces new datasets with ground-truth arrangements: 1) 2D Voronoi jigsaw dataset, a synthetic one where pieces are generated by Voronoi diagram of 2D pointset; and 2) MagicPlan dataset, a real one offered by MagicPlan from its production pipeline, where pieces are room layouts constructed by augmented reality App by real-estate consumers. The qualitative and quantitative evaluations demonstrate that our approach outperforms the competing methods by significant margins in all the tasks.

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