mlsanityData to 7 Oct 2026

SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

arXiv 1703.01661 ↗First posted 5 Mar 2017cs.RO cs.CVcited by 29

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Abstract (arXiv)

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated solution to object recognition and pose estimation. SegICP couples convolutional neural networks and multi-hypothesis point cloud registration to achieve both robust pixel-wise semantic segmentation as well as accurate and real-time 6-DOF pose estimation for relevant objects. Our architecture achieves 1cm position error and <5^circ angle error in real time without an initial seed. We evaluate and benchmark SegICP against an annotated dataset generated by motion capture.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 5; 2023: 6; 2024: 4; 2025: 2; 2026: 3.

29 papers.

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  1. 2022RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wildcited by 102
  2. 2023RelPose++: Recovering 6D Poses from Sparse-view Observationscited by 87
  3. 2025Any6D: Model-free 6D Pose Estimation of Novel Objectscited by 47
  4. 2023Robust Outlier Rejection for 3D Registration with Variational Bayescited by 31
  5. 2021UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimationcited by 25
  6. 2023FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Networkcited by 22
  7. 2022Reliable Inlier Evaluation for Unsupervised Point Cloud Registrationcited by 19
  8. 2021TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratioscited by 11
  9. 2022Self-Supervised Point Cloud Registration with Deep Versatile Descriptorscited by 10
  10. 2021Robust Partial-to-Partial Point Cloud Registration in a Full Rangecited by 8
  11. 2021Planning with Learned Dynamic Model for Unsupervised Point Cloud Registrationcited by 7
  12. 2024Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimationcited by 7
17 more
  1. 2024Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Cloudscited by 7
  2. 2021Point Cloud Registration using Representative Overlapping Pointscited by 5
  3. 2021DANIEL: A Fast and Robust Consensus Maximization Method for Point Cloud Registration with High Outlier Ratioscited by 5
  4. 2021End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registrationcited by 5
  5. 2026FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real timecited by 5
  6. 2021Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Pickingcited by 4
  7. 2023GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenescited by 4
  8. 2022Learning a Task-specific Descriptor for Robust Matching of 3D Point Cloudscited by 3
  9. 2023CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Datacited by 3
  10. 2023Overlap Bias Matching is Necessary for Point Cloud Registrationcited by 2
  11. 2021Deep 6-DoF Tracking of Unknown Objects for Reactive Graspingcited by 1
  12. 2022Outlier-Robust Geometric Perception: A Novel Thresholding-Based Estimator with Intra-Class Variance Maximizationcited by 0
  13. 2024Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registrationcited by 0
  14. 2024Correspondence Free Multivector Cloud Registration using Conformal Geometric Algebracited by 0
  15. 2025STORM: Segment, Track, and Object Re-Localization from a Single Imagecited by 0
  16. 2026UniPose9D: Universal Category-Agnostic Object Pose Estimationcited by 0
  17. 2026PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registrationcited by 0