mlsanityData to 14 Oct 2026

Score-PA: Score-based 3D Part Assembly

arXiv 2309.04220 ↗First posted 8 Sep 2023cs.CVcited by 12

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Abstract (arXiv)

Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 20 of its 33 references that are in the map. First posted on arXiv 8 Sep 2023.

Builds on

20 of 33 references.

References · 20

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,043
  6. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 672
  7. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 526
  8. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 384
  9. 2020Learning Gradient Fields for Shape Generationcited by 245
  10. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  11. 2021Learning Gradient Fields for Molecular Conformation Generationcited by 163
  12. 2019PQ-NET: A Generative Part Seq2Seq Network for 3D Shapescited by 119
8 more
  1. 2021Energy-Based Learning for Scene Graph Generationcited by 104
  2. 2021Score-Based Point Cloud Denoisingcited by 83
  3. 2020Generative 3D Part Assembly via Dynamic Graph Learningcited by 67
  4. 2022GFPose: Learning 3D Human Pose Prior with Gradient Fieldscited by 57
  5. 2020Learning 3D Part Assembly from a Single Imagecited by 52
  6. 2021RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assemblycited by 36
  7. 2019Composite Shape Modeling via Latent Space Factorizationcited by 31
  8. 2020PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditionscited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 2; 2026: 3.

12 papers.

  1. 2024Omni6DPose: A Benchmark and Model for Universal 6D Object Pose Estimation and Trackingcited by 50
  2. 2024When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Modelscited by 43
  3. 2024Generative 3D Part Assembly via Part-Whole-Hierarchy Message Passingcited by 19
  4. 2025Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulationcited by 13
  5. 2024SPAFormer: Sequential 3D Part Assembly with Transformerscited by 12
  6. 2024Manual-PA: Learning 3D Part Assembly from Instruction Diagramscited by 7
  7. 2024Geometric Point Attention Transformer for 3D Shape Reassemblycited by 3
  8. 2024G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Groupingcited by 0
  9. 2025Leveraging Pretrained Diffusion Models for Zero-Shot Part Assemblycited by 0
  10. 2026PART: Learning 3D Part Assembly and Retrieval with Transformerscited by 0
  11. 2026AssemblyWorld: Rethinking 3D Assembly with General-Purpose Agentscited by 0
  12. 2026LLM2R: From Large Language Models to 3D Reassemblerscited by 0