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Geometric fracture assembly presents a challenging practical task inarchaeology and 3D computer vision. Previous methods have focused solely onassembling fragments based on semantic information, which has limited thequantity of objects that can be effectively assembled. Therefore, there is aneed to develop a scalable framework for geometric fracture assembly withoutrelying on semantic information. To improve the effectiveness of assemblinggeometric fractures without semantic information, we propose a co-creationspace comprising several assemblers capable of gradually and unambiguouslyassembling fractures. Additionally, we introduce a novel loss function, i.e.,the geometric-based collision loss, to address collision issues during thefracture assembly process and enhance the results. Our framework exhibitsbetter performance on both PartNet and Breaking Bad datasets compared toexisting state-of-the-art frameworks. Extensive experiments and quantitativecomparisons demonstrate the effectiveness of our proposed framework, whichfeatures linear computational complexity, enhanced abstraction, and improvedgeneralization. Our code is publicly available athttps://github.com/Ruiyuan-Zhang/CCS.

Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers