Reassembling Broken Objects using Breaking Curves
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Abstract (arXiv)
Reassembling 3D broken objects is a challenging task. A robust solution that generalizes well must deal with diverse patterns associated with different types of broken objects. We propose a method that tackles the pairwise assembly of 3D point clouds, that is agnostic on the type of object, and that relies solely on their geometrical information, without any prior information on the shape of the reconstructed object. The method receives two point clouds as input and segments them into regions using detected closed boundary contours, known as breaking curves. Possible alignment combinations of the regions of each broken object are evaluated and the best one is selected as the final alignment. Experiments were carried out both on available 3D scanned objects and on a recent benchmark for synthetic broken objects. Results show that our solution performs well in reassembling different kinds of broken objects.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 5 Jun 2023.
Builds on
10 of 23 references.
References · 10
- 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
- 2020TEASER: Fast and Certifiable Point Cloud Registrationcited by 274
- 2018EC-Net: an Edge-aware Point set Consolidation Networkcited by 84
- 2020Generative 3D Part Assembly via Dynamic Graph Learningcited by 65
- 2022Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priorscited by 42
- 2022Breaking Bad: A Dataset for Geometric Fracture and Reassemblycited by 38
- 2022Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learningcited by 21
- 2021RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environmentcited by 13
- 2022Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learningcited by 13
- 2020Learning Part Boundaries from 3D Point Cloudscited by 9
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