mlsanityData to 7 Oct 2026

Real-time Simultaneous Multi-Object 3D Shape Reconstruction, 6DoF Pose Estimation and Dense Grasp Prediction

arXiv 2305.09510 ↗First posted 16 May 2023cs.RO cs.AI cs.CVcited by 2

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Abstract (arXiv)

Robotic manipulation systems operating in complex environments rely on perception systems that provide information about the geometry (pose and 3D shape) of the objects in the scene along with other semantic information such as object labels. This information is then used for choosing the feasible grasps on relevant objects. In this paper, we present a novel method to provide this geometric and semantic information of all objects in the scene as well as feasible grasps on those objects simultaneously. The main advantage of our method is its speed as it avoids sequential perception and grasp planning steps. With detailed quantitative analysis, we show that our method delivers competitive performance compared to the state-of-the-art dedicated methods for object shape, pose, and grasp predictions while providing fast inference at 30 frames per second speed.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 34 of its 38 references that are in the map. First posted on arXiv 16 May 2023.

Builds on

34 of 38 references.

References · 34

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  3. 2017Mask R-CNNcited by 7,259
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  8. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 768
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  11. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  12. 2020Transporter Networks: Rearranging the Visual World for Robotic Manipulationcited by 342
22 more
  1. 2019Towards Real-Time Multi-Object Trackingcited by 311
  2. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
  3. 2018Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objectscited by 237
  4. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 237
  5. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 213
  6. 2020Shape Prior Deformation for Categorical 6D Object Pose and Size Estimationcited by 158
  7. 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 124
  8. 2017Grasp Pose Detection in Point Cloudscited by 107
  9. 2021Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representationscited by 107
  10. 2021Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Cluttercited by 94
  11. 2016Shape Completion Enabled Robotic Graspingcited by 79
  12. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 70
  13. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 69
  14. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 38
  15. 2019Learning Continuous 3D Reconstructions for Geometrically Aware Graspingcited by 28
  16. 2018Pixels, voxels, and views: A study of shape representations for single view 3D object shape predictioncited by 27
  17. 2022MonoGraspNet: 6-DoF Grasping with a Single RGB Imagecited by 24
  18. 2022Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D inputcited by 10
  19. 2021Simultaneous Object Reconstruction and Grasp Prediction using a Camera-centric Object Shell Representationcited by 7
  20. 2019Higher-Order Function Networks for Learning Composable 3D Object Representationscited by 6
  21. 2023KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D inputcited by 4
  22. 2023Pick2Place: Task-aware 6DoF Grasp Estimation via Object-Centric Perspective Affordancecited by 4

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimationcited by 13
  2. 2025A Segmented Robot Grasping Perception Neural Network for Edge AIcited by 1