# Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterparts

arXiv: https://arxiv.org/abs/2303.14152 · HTML: https://mlsanity.com/paper/2303.14152/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 62 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 23 of 23)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding](https://mlsanity.com/paper/1812.02713/) (2018)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Thingi10K: A Dataset of 10,000 3D-Printing Models](https://mlsanity.com/paper/1605.04797/) (2016)
- [PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes](https://mlsanity.com/paper/1911.10949/) (2019)
- [Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols](https://mlsanity.com/paper/1502.03143/) (2015)
- [AKB-48: A Real-World Articulated Object Knowledge Base](https://mlsanity.com/paper/2202.08432/) (2022)
- [Generative 3D Part Assembly via Dynamic Graph Learning](https://mlsanity.com/paper/2006.07793/) (2020)
- [A Large Dataset of Object Scans](https://mlsanity.com/paper/1602.02481/) (2016)
- [ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes](https://mlsanity.com/paper/2106.02740/) (2021)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [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)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly](https://mlsanity.com/paper/2107.12859/) (2021)
- [3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network](https://mlsanity.com/paper/1711.06363/) (2017)
- [DeepMend: Learning Occupancy Functions to Represent Shape for Repair](https://mlsanity.com/paper/2210.05728/) (2022)
- [DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair](https://mlsanity.com/paper/2211.12400/) (2022)
- [Remeshing-Free Graph-Based Finite Element Method for Ductile and Brittle Fracture](https://mlsanity.com/paper/2103.14870/) (2021)

## Built on by (most cited first, 18 of 18)

- [Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving](https://mlsanity.com/paper/2410.24010/) (2024)
- [PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify](https://mlsanity.com/paper/2406.00259/) (2024)
- [GARF: Learning Generalizable 3D Reassembly for Real-World Fractures](https://mlsanity.com/paper/2504.05400/) (2025)
- [SPAFormer: Sequential 3D Part Assembly with Transformers](https://mlsanity.com/paper/2403.05874/) (2024)
- ["Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations](https://mlsanity.com/paper/2404.08827/) (2024)
- [A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts](https://mlsanity.com/paper/2410.14770/) (2024)
- [Jigsaw++: Imagining Complete Shape Priors for Object Reassembly](https://mlsanity.com/paper/2410.11816/) (2024)
- [TORA: Topological Representation Alignment for 3D Shape Assembly](https://mlsanity.com/paper/2604.04050/) (2026)
- [RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects](https://mlsanity.com/paper/2504.02465/) (2025)
- [DeepFracture: A Generative Approach for Predicting Brittle Fractures with Neural Discrete Representation Learning](https://mlsanity.com/paper/2310.13344/) (2023)
- [E-M3RF: An Equivariant Multimodal 3D Re-assembly Framework](https://mlsanity.com/paper/2511.21422/) (2025)
- [SARe: Structure-Aware Generative 3D Fragment Reassembly](https://mlsanity.com/paper/2603.21611/) (2026)
- [Pix2Repair: Implicit Shape Restoration from Images](https://mlsanity.com/paper/2305.18273/) (2023)
- [3D Shape Completion with Test-Time Training](https://mlsanity.com/paper/2410.18668/) (2024)
- [Neural Clustering for Prefractured Mesh Generation in Real-time Object Destruction](https://mlsanity.com/paper/2502.04615/) (2025)
- [TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder](https://mlsanity.com/paper/2512.11926/) (2025)
- [Restore3D: Breathing Life into Broken Objects with Shape and Texture Restoration](https://mlsanity.com/paper/2607.00522/) (2026)
- [AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agents](https://mlsanity.com/paper/2609.40353/) (2026)

## Abstract (arXiv)

Automated shape repair approaches currently lack access to datasets that describe real-world damaged geometry. We present Fantastic Breaks (and Where to Find Them: https://terascale-all-sensing-research-studio.github.io/FantasticBreaks), a dataset containing scanned, waterproofed, and cleaned 3D meshes for 150 broken objects, paired and geometrically aligned with complete counterparts. Fantastic Breaks contains class and material labels, proxy repair parts that join to broken meshes to generate complete meshes, and manually annotated fracture boundaries. Through a detailed analysis of fracture geometry, we reveal differences between Fantastic Breaks and synthetic fracture datasets generated using geometric and physics-based methods. We show experimental shape repair evaluation with Fantastic Breaks using multiple learning-based approaches pre-trained with synthetic datasets and re-trained with subset of Fantastic Breaks.