mlsanityData to 7 Oct 2026

Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation

arXiv 2104.07528 ↗First posted 15 Apr 2021cs.CV cs.AI cs.ROcited by 3

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Abstract (arXiv)

Single shot approaches have demonstrated tremendous success on various computer vision tasks. Finding good parameterizations for 6D object pose estimation remains an open challenge. In this work, we propose different novel parameterizations for the output of the neural network for single shot 6D object pose estimation. Our learning-based approach achieves state-of-the-art performance on two public benchmark datasets. Furthermore, we demonstrate that the pose estimates can be used for real-world robotic grasping tasks without additional ICP refinement.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 34 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

24 of 34 references.

References · 24

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  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
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  7. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  8. 2019YOLACT: Real-time Instance Segmentationcited by 441
  9. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  10. 2020CosyPose: Consistent multi-view multi-object 6D pose estimationcited by 287
  11. 2018BOP: Benchmark for 6D Object Pose Estimationcited by 254
  12. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
12 more
  1. 2017BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depthcited by 225
  2. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  3. 2020BOP Challenge 2020 on 6D Object Localizationcited by 192
  4. 2020Convolutional Networks with Dense Connectivitycited by 98
  5. 2019Single-Stage 6D Object Pose Estimationcited by 88
  6. 2019YOLACT++: Better Real-time Instance Segmentationcited by 84
  7. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25
  8. 2020Single Shot 6D Object Pose Estimationcited by 19
  9. 2016Defining the Pose of any 3D Rigid Object and an Associated Distancecited by 17
  10. 2018Symmetry Aware Evaluation of 3D Object Detection and Pose Estimation in Scenes of Many Parts in Bulkcited by 12
  11. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  12. 2018Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarioscited by 5

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.

3 papers.

  1. 2023Pseudo-keypoint RKHS Learning for Self-supervised 6DoF Pose Estimationcited by 4
  2. 2024SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarioscited by 3
  3. 2021Precise Object Placement with Pose Distance Estimations for Different Objects and Gripperscited by 0