# SegICP: Integrated Deep Semantic Segmentation and Pose Estimation

arXiv: https://arxiv.org/abs/1703.01661 · HTML: https://mlsanity.com/paper/1703.01661/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 29 of 29)

- [RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild](https://mlsanity.com/paper/2208.05963/) (2022)
- [RelPose++: Recovering 6D Poses from Sparse-view Observations](https://mlsanity.com/paper/2305.04926/) (2023)
- [Any6D: Model-free 6D Pose Estimation of Novel Objects](https://mlsanity.com/paper/2503.18673/) (2025)
- [Robust Outlier Rejection for 3D Registration with Variational Bayes](https://mlsanity.com/paper/2304.01514/) (2023)
- [UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation](https://mlsanity.com/paper/2111.12580/) (2021)
- [FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network](https://mlsanity.com/paper/2301.08160/) (2023)
- [Reliable Inlier Evaluation for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2202.11292/) (2022)
- [TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios](https://mlsanity.com/paper/2111.00657/) (2021)
- [Self-Supervised Point Cloud Registration with Deep Versatile Descriptors](https://mlsanity.com/paper/2201.10034/) (2022)
- [Robust Partial-to-Partial Point Cloud Registration in a Full Range](https://mlsanity.com/paper/2111.15606/) (2021)
- [Planning with Learned Dynamic Model for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2108.02613/) (2021)
- [Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation](https://mlsanity.com/paper/2401.10848/) (2024)
- [Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds](https://mlsanity.com/paper/2402.10865/) (2024)
- [Point Cloud Registration using Representative Overlapping Points](https://mlsanity.com/paper/2107.02583/) (2021)
- [DANIEL: A Fast and Robust Consensus Maximization Method for Point Cloud Registration with High Outlier Ratios](https://mlsanity.com/paper/2110.05075/) (2021)
- [End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration](https://mlsanity.com/paper/2110.15250/) (2021)
- [FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time](https://mlsanity.com/paper/2601.05738/) (2026)
- [Object detection and Autoencoder-based 6D pose estimation for highly cluttered Bin Picking](https://mlsanity.com/paper/2106.08045/) (2021)
- [GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes](https://mlsanity.com/paper/2312.03345/) (2023)
- [Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds](https://mlsanity.com/paper/2210.14899/) (2022)
- [CHSEL: Producing Diverse Plausible Pose Estimates from Contact and Free Space Data](https://mlsanity.com/paper/2305.08042/) (2023)
- [Overlap Bias Matching is Necessary for Point Cloud Registration](https://mlsanity.com/paper/2308.09364/) (2023)
- [Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping](https://mlsanity.com/paper/2103.05401/) (2021)
- [Outlier-Robust Geometric Perception: A Novel Thresholding-Based Estimator with Intra-Class Variance Maximization](https://mlsanity.com/paper/2204.01324/) (2022)
- [Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration](https://mlsanity.com/paper/2405.08991/) (2024)
- [Correspondence Free Multivector Cloud Registration using Conformal Geometric Algebra](https://mlsanity.com/paper/2406.11732/) (2024)
- [STORM: Segment, Track, and Object Re-Localization from a Single Image](https://mlsanity.com/paper/2511.09771/) (2025)
- [UniPose9D: Universal Category-Agnostic Object Pose Estimation](https://mlsanity.com/paper/2607.09985/) (2026)
- [PARTE: Plane-Assisted Robust Transformation Estimation for Point Cloud Registration](https://mlsanity.com/paper/2609.25375/) (2026)

## 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.