mlsanityData to 7 Oct 2026

Context-aware 6D Pose Estimation of Known Objects using RGB-D data

arXiv 2212.05560 ↗First posted 11 Dec 2022cs.CV cs.ROcited by 0

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Abstract (arXiv)

6D object pose estimation has been a research topic in the field of computer vision and robotics. Many modern world applications like robot grasping, manipulation, autonomous navigation etc, require the correct pose of objects present in a scene to perform their specific task. It becomes even harder when the objects are placed in a cluttered scene and the level of occlusion is high. Prior works have tried to overcome this problem but could not achieve accuracy that can be considered reliable in real-world applications. In this paper, we present an architecture that, unlike prior work, is context-aware. It utilizes the context information available to us about the objects. Our proposed architecture treats the objects separately according to their types i.e; symmetric and non-symmetric. A deeper estimator and refiner network pair is used for non-symmetric objects as compared to symmetric due to their intrinsic differences. Our experiments show an enhancement in the accuracy of about 3.2% over the LineMOD dataset, which is considered a benchmark for pose estimation in the occluded and cluttered scenes, against the prior state-of-the-art DenseFusion. Our results also show that the inference time we got is sufficient for real-time usage.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 11 Dec 2022.

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18 of 34 references.

References · 18

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  3. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
  4. 2017Frustum PointNets for 3D Object Detection from RGB-D Datacited by 640
  5. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  6. 2018DeepIM: Deep Iterative Matching for 6D Pose Estimationcited by 283
  7. 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 277
  8. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
  9. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  10. 2017PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimationcited by 170
  11. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  12. 2015Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Imagescited by 111
6 more
  1. 20176-DoF Object Pose from Semantic Keypointscited by 89
  2. 2018Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoningcited by 79
  3. 2015Learning Descriptors for Object Recognition and 3D Pose Estimationcited by 60
  4. 2018A Unified Framework for Multi-View Multi-Class Object Pose Estimationcited by 49
  5. 2016Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimationcited by 34
  6. 2016Subcategory-aware Convolutional Neural Networks for Object Proposals and Detectioncited by 17

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