GFPack++: Attention-Driven Gradient Fields for Optimizing 2D Irregular Packing

Abstract:2D irregular packing is a classic combinatorial optimization problem with various applications, such as material utilization and texture atlas generation. Due to its NP-hard nature, conventional numerical approaches typically encounter slow convergence and high computational costs. Previous research (GFPack) introduced a generative method for gradient-based packing, providing early evidence of its feasibility but faced limitations such as insufficient rotation support, poor boundary adaptability, and high overlap ratios. In this paper, we propose GFPack++, a deeply investigated framework that adopts attention-based geometry and relation encoding, enabling more comprehensive modeling of complex packing relationships. We further design a constrained gradient and a weighting function to enhance both the feasibility of the produced solutions and the learning effectiveness. Experimental results on multiple datasets demonstrate that GFPack++ achieves higher space utilization, supports continuous rotation, generalizes well to arbitrary boundaries, and infers orders of magnitude faster than previous approaches. Codes for this paper are at this https URL.

14 pages including supplementary material; updated to the ICCV 2025 version with revised title and editorial corrections. Code: this https URL

Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)

arXiv:2406.07579 [cs.AI]

(or arXiv:2406.07579v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2406.07579 Focus to learn more arXiv-issued DOI via DataCite

Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 18014-18023

https://doi.org/10.1109/ICCV51701.2025.01674 Focus to learn more DOI(s) linking to related resources

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