  Geometric fracture assembly presents a challenging practical task in
archaeology and 3D computer vision. Previous methods have focused solely on
assembling fragments based on semantic information, which has limited the
quantity of objects that can be effectively assembled. Therefore, there is a
need to develop a scalable framework for geometric fracture assembly without
relying on semantic information. To improve the effectiveness of assembling
geometric fractures without semantic information, we propose a co-creation
space comprising several assemblers capable of gradually and unambiguously
assembling fractures. Additionally, we introduce a novel loss function, i.e.,
the geometric-based collision loss, to address collision issues during the
fracture assembly process and enhance the results. Our framework exhibits
better performance on both PartNet and Breaking Bad datasets compared to
existing state-of-the-art frameworks. Extensive experiments and quantitative
comparisons demonstrate the effectiveness of our proposed framework, which
features linear computational complexity, enhanced abstraction, and improved
generalization. Our code is publicly available at
https://github.com/Ruiyuan-Zhang/CCS.
