mlsanityData to 13 Oct 2026

PVRA: A Pointwise Key-point Voting Framework for Robotic Assembly

arXiv 2608.19968 ↗First posted 20 Aug 2026cs.RO cs.CVcited by 0

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Abstract (arXiv)

Modern computer vision has enabled partial autonomy in robotic assembly manipulation. However, performing autonomous manipulation of a progressive assembly demands a more specific set of skills, in addition to perceiving the objects. Through a comparative analysis of research in the associated domains, we deduce that object-centric perception must advance towards learning assembly dependencies to predict meaningful actionable outputs for autonomous assembly manipulation. Subsequently, we present a 3D keypoint-based modular learning framework to learn assembly dependencies to infer actionable outputs given a RGB-D input of an assembly scene. We train and evaluate our trained network on an assembly pose estimation dataset and compare it against object-centric baselines with an augmented set of metrics for progressive assemblies.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 21 of its 25 references that are in the map. First posted on arXiv 20 Aug 2026.

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21 of 25 references.

References · 21

  1. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,889
  2. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  3. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  4. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 605
  5. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 524
  6. 2023FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objectscited by 415
  7. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  8. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 354
  9. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  10. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  11. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 182
  12. 2020Generative 3D Part Assembly via Dynamic Graph Learningcited by 65
9 more
  1. 2022Neural Shape Mating: Self-Supervised Object Assembly with Adversarial Shape Priorscited by 42
  2. 2022Breaking Bad: A Dataset for Geometric Fracture and Reassemblycited by 38
  3. 2021RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assemblycited by 35
  4. 2023Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assemblycited by 27
  5. 20223D Part Assembly Generation with Instance Encoded Transformercited by 17
  6. 2023ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibilitycited by 17
  7. 2023Rearrangement Planning for General Part Assemblycited by 12
  8. 2022Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassemblycited by 6
  9. 2023Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selectioncited by 5

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