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Learning 3D Part Assembly from a Single Image

arXiv 2003.09754 ↗First posted 21 Mar 2020cs.CV cs.ROcited by 52

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Abstract (arXiv)

Autonomous assembly is a crucial capability for robots in many applications. For this task, several problems such as obstacle avoidance, motion planning, and actuator control have been extensively studied in robotics. However, when it comes to task specification, the space of possibilities remains underexplored. Towards this end, we introduce a novel problem, single-image-guided 3D part assembly, along with a learningbased solution. We study this problem in the setting of furniture assembly from a given complete set of parts and a single image depicting the entire assembled object. Multiple challenges exist in this setting, including handling ambiguity among parts (e.g., slats in a chair back and leg stretchers) and 3D pose prediction for parts and part subassemblies, whether visible or occluded. We address these issues by proposing a two-module pipeline that leverages strong 2D-3D correspondences and assembly-oriented graph message-passing to infer part relationships. In experiments with a PartNet-based synthetic benchmark, we demonstrate the effectiveness of our framework as compared with three baseline approaches.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 6; 2023: 11; 2024: 13; 2025: 8; 2026: 5.

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  1. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 103
  2. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 78
  3. 2022Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priorscited by 43
  4. 2022Breaking Bad: A Dataset for Geometric Fracture and Reassemblycited by 41
  5. 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 40
  6. 2021RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assemblycited by 36
  7. 2023PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervisioncited by 36
  8. 2024DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassemblycited by 33
  9. 2023Jigsaw: Learning to Assemble Multiple Fractured Objectscited by 29
  10. 2023Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assemblycited by 28
  11. 2025PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understandingcited by 26
  12. 2023IKEA-Manual: Seeing Shape Assembly Step by Stepcited by 22
38 more
  1. 2023Category-Level Multi-Part Multi-Joint 3D Shape Assemblycited by 22
  2. 2021Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learningcited by 19
  3. 20223D Part Assembly Generation with Instance Encoded Transformercited by 19
  4. 2024Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passingcited by 19
  5. 2024Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solvingcited by 19
  6. 2023ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibilitycited by 18
  7. 2022Translating a Visual LEGO Manual to a Machine-Executable Plancited by 15
  8. 2021RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environmentcited by 14
  9. 2023Scalable Geometric Fracture Assembly via Co-creation Space among Assemblerscited by 14
  10. 2025Manual2Skill: Learning to Read Manuals and Acquire Robotic Skills for Furniture Assembly Using Vision-Language Modelscited by 14
  11. 2021AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assembliescited by 13
  12. 2021Discovering 3D Parts from Image Collectionscited by 13
  13. 2025Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulationcited by 13
  14. 2023Rearrangement Planning for General Part Assemblycited by 12
  15. 2023Score-PA: Score-based 3D Part Assemblycited by 12
  16. 2024SPAFormer: Sequential 3D Part Assembly with Transformerscited by 12
  17. 2024IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videoscited by 12
  18. 20243D Geometric Shape Assembly via Efficient Point Cloud Matchingcited by 9
  19. 2024A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Partscited by 9
  20. 2022Break and Make: Interactive Structural Understanding Using LEGO Brickscited by 8
  21. 2021Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecturecited by 7
  22. 2024Manual-PA: Learning 3D Part Assembly from Instruction Diagramscited by 7
  23. 2023Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selectioncited by 6
  24. 2024Jigsaw++: Imagining Complete Shape Priors for Object Reassemblycited by 6
  25. 2021Learning Mesh Representations via Binary Space Partitioning Tree Networkscited by 5
  26. 2024Neural Assembler: Learning to Generate Fine-Grained Robotic Assembly Instructions from Multi-View Imagescited by 5
  27. 2025Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Modelscited by 5
  28. 2024TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assemblycited by 3
  29. 2024Geometric Point Attention Transformer for 3D Shape Reassemblycited by 3
  30. 2025Combinative Matching for Geometric Shape Assemblycited by 3
  31. 2025HierOctFusion: Multi-scale Octree-based 3D Shape Generation via Part-Whole-Hierarchy Message Passingcited by 3
  32. 2026A3D: Adaptive Affordance Assembly with Dual-Arm Manipulationcited by 3
  33. 2022AssembleRL: Learning to Assemble Furniture from Their Point Cloudscited by 2
  34. 2026AssemblyBench: Physics-Aware Assembly of Complex Industrial Objectscited by 2
  35. 2024Component Selection for Craft Assembly Taskscited by 1
  36. 2025E-M3RF: An Equivariant Multimodal 3D Re-assembly Frameworkcited by 1
  37. 2025Prompt2Craft: Generating Functional Craft Assemblies with LLMscited by 0
  38. 2026PART: Learning 3D Part Assembly and Retrieval with Transformerscited by 0

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