# Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers

arXiv: https://arxiv.org/abs/2312.12340 · HTML: https://mlsanity.com/paper/2312.12340/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization](https://mlsanity.com/paper/2106.06607/) (2021)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes](https://mlsanity.com/paper/1911.10949/) (2019)
- [Coordination Among Neural Modules Through a Shared Global Workspace](https://mlsanity.com/paper/2103.01197/) (2021)
- [IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks](https://mlsanity.com/paper/1911.07246/) (2019)
- [Generative 3D Part Assembly via Dynamic Graph Learning](https://mlsanity.com/paper/2006.07793/) (2020)
- [Modular Networks: Learning to Decompose Neural Computation](https://mlsanity.com/paper/1811.05249/) (2018)
- [Learning 3D Part Assembly from a Single Image](https://mlsanity.com/paper/2003.09754/) (2020)
- [Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priors](https://mlsanity.com/paper/2205.14886/) (2022)
- [Breaking Bad: A Dataset for Geometric Fracture and Reassembly](https://mlsanity.com/paper/2210.11463/) (2022)
- [RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly](https://mlsanity.com/paper/2107.12859/) (2021)
- [Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding](https://mlsanity.com/paper/1809.03702/) (2018)
- [Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly](https://mlsanity.com/paper/2309.06810/) (2023)
- [3D Part Assembly Generation with Instance Encoded Transformer](https://mlsanity.com/paper/2207.01779/) (2022)
- [Universal Domain Adaptation via Compressive Attention Matching](https://mlsanity.com/paper/2304.11862/) (2023)

## Built on by (most cited first, 14 of 14)

- [MedCoT: Medical Chain of Thought via Hierarchical Expert](https://mlsanity.com/paper/2412.13736/) (2024)
- [GARF: Learning Generalizable 3D Reassembly for Real-World Fractures](https://mlsanity.com/paper/2504.05400/) (2025)
- [Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulation](https://mlsanity.com/paper/2504.06961/) (2025)
- [SPAFormer: Sequential 3D Part Assembly with Transformers](https://mlsanity.com/paper/2403.05874/) (2024)
- [Assembler: Scalable 3D Part Assembly via Anchor Point Diffusion](https://mlsanity.com/paper/2506.17074/) (2025)
- [A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts](https://mlsanity.com/paper/2410.14770/) (2024)
- [Manual-PA: Learning 3D Part Assembly from Instruction Diagrams](https://mlsanity.com/paper/2411.18011/) (2024)
- [MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing](https://mlsanity.com/paper/2411.16773/) (2024)
- [TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis](https://mlsanity.com/paper/2505.14910/) (2025)
- [SGW-based Multi-Task Learning in Vision Tasks](https://mlsanity.com/paper/2410.03778/) (2024)
- [PVP: Polar Representation Boost for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2412.07616/) (2024)
- [Leveraging Pretrained Diffusion Models for Zero-Shot Part Assembly](https://mlsanity.com/paper/2505.00426/) (2025)
- [AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agents](https://mlsanity.com/paper/2609.40353/) (2026)
- [LLM2R: From Large Language Models to 3D Reassemblers](https://mlsanity.com/paper/2610.18604/) (2026)

## Abstract (arXiv)

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.