mlsanityData to 13 Oct 2026

Multistream ValidNet: Improving 6D Object Pose Estimation by Automatic Multistream Validation

arXiv 2106.06684 ↗First posted 12 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Sil'eane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 12 Jun 2021.

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11 of 21 references.

References · 11

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,231
  3. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 770
  4. 20163DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructionscited by 334
  5. 2018A Unified Framework for Multi-View Multi-Class Object Pose Estimationcited by 49
  6. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25
  7. 2020Single Shot 6D Object Pose Estimationcited by 19
  8. 2016Defining the Pose of any 3D Rigid Object and an Associated Distancecited by 18
  9. 2018Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulkcited by 12
  10. 2019CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimationcited by 9
  11. 2018Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarioscited by 5

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