mlsanityData to 14 Oct 2026

6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly

arXiv 2505.24669 ↗First posted 30 May 2025cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of product life cycles. A pivotal objective in this context involves the identification and 6D pose estimation of bolts affixed to the motors, facilitating automated disassembly within the manufacturing workflow. Complicating matters, the presence of occlusions and the limitations of single-view data acquisition, notably when motors are placed in a clamping system, obscure certain portions and render some bolts imperceptible. Consequently, the development of a comprehensive pipeline capable of acquiring complete bolt information is imperative to avoid oversight in bolt detection. In this paper, employing the task of bolt detection within the scope of our project as a pertinent use case, we introduce a meticulously devised pipeline. This multi-stage pipeline effectively captures the 6D information with regard to all bolts on the motor, thereby showcasing the effective utilization of prior knowledge in handling this challenging task. The proposed methodology not only contributes to the field of 6D pose estimation but also underscores the viability of integrating domain-specific insights to tackle complex problems in manufacturing and automation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

17 of 35 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  5. 2020PCT: Point cloud transformercited by 732
  6. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 724
  7. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  8. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  9. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  10. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 355
  11. 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 326
  12. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
5 more
  1. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  2. 2023Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassemblycited by 16
  3. 2022MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Networkcited by 9
  4. 2023MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motorscited by 2
  5. 2023SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learningcited by 1

Built on by

Not cited yet.