# Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

arXiv: https://arxiv.org/abs/2202.11292 · HTML: https://mlsanity.com/paper/2202.11292/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 58 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PREDATOR: Registration of 3D Point Clouds with Low Overlap](https://mlsanity.com/paper/2011.13005/) (2020)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [Deep Global Registration](https://mlsanity.com/paper/2004.11540/) (2020)
- [RPM-Net: Robust Point Matching using Learned Features](https://mlsanity.com/paper/2003.13479/) (2020)
- [PRNet: Self-Supervised Learning for Partial-to-Partial Registration](https://mlsanity.com/paper/1910.12240/) (2019)
- [PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency](https://mlsanity.com/paper/2103.05465/) (2021)
- [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](https://mlsanity.com/paper/2005.01014/) (2020)
- [Robust Point Cloud Registration Framework Based on Deep Graph Matching](https://mlsanity.com/paper/2103.04256/) (2021)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [DeepGMR: Learning Latent Gaussian Mixture Models for Registration](https://mlsanity.com/paper/2008.09088/) (2020)
- [Learning multiview 3D point cloud registration](https://mlsanity.com/paper/2001.05119/) (2020)
- [DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration](https://mlsanity.com/paper/1905.04153/) (2019)
- [USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds](https://mlsanity.com/paper/1904.00229/) (2019)
- [PCRNet: Point Cloud Registration Network using PointNet Encoding](https://mlsanity.com/paper/1908.07906/) (2019)
- [HRegNet: A Hierarchical Network for Large-scale Outdoor LiDAR Point Cloud Registration](https://mlsanity.com/paper/2107.11992/) (2021)
- [UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering](https://mlsanity.com/paper/2102.11870/) (2021)
- [IMLS-SLAM: scan-to-model matching based on 3D data](https://mlsanity.com/paper/1802.08633/) (2018)
- [SegICP: Integrated Deep Semantic Segmentation and Pose Estimation](https://mlsanity.com/paper/1703.01661/) (2017)
- [SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences](https://mlsanity.com/paper/1904.03483/) (2019)
- [Sampling Network Guided Cross-Entropy Method for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2109.06619/) (2021)
- [Unsupervised cycle-consistent deformation for shape matching](https://mlsanity.com/paper/1907.03165/) (2019)
- [ReAgent: Point Cloud Registration using Imitation and Reinforcement Learning](https://mlsanity.com/paper/2103.15231/) (2021)
- [Unsupervised Point Cloud Registration via Salient Points Analysis (SPA)](https://mlsanity.com/paper/2009.01293/) (2020)
- [Recurrent Multi-view Alignment Network for Unsupervised Surface Registration](https://mlsanity.com/paper/2011.12104/) (2020)
- [Mapping in a cycle: Sinkhorn regularized unsupervised learning for point cloud shapes](https://mlsanity.com/paper/2007.09594/) (2020)
- [MaskNet: A Fully-Convolutional Network to Estimate Inlier Points](https://mlsanity.com/paper/2010.09185/) (2020)
- [RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representation](https://mlsanity.com/paper/2104.05328/) (2021)

## Built on by (most cited first, 20 of 20)

- [SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation](https://mlsanity.com/paper/2310.17359/) (2023)
- [Robust Outlier Rejection for 3D Registration with Variational Bayes](https://mlsanity.com/paper/2304.01514/) (2023)
- [Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration](https://mlsanity.com/paper/2303.13290/) (2023)
- [Overlap-guided Gaussian Mixture Models for Point Cloud Registration](https://mlsanity.com/paper/2210.09836/) (2022)
- [UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration](https://mlsanity.com/paper/2208.02712/) (2022)
- [EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling](https://mlsanity.com/paper/2412.17249/) (2024)
- [Unsupervised Point Cloud Registration with Self-Distillation](https://mlsanity.com/paper/2409.07558/) (2024)
- [FUSER: Feed-Forward MUltiview 3D Registration Transformer and SE(3)N Diffusion Refinement](https://mlsanity.com/paper/2512.09373/) (2025)
- [Graph Matching Optimization Network for Point Cloud Registration](https://mlsanity.com/paper/2302.05844/) (2023)
- [Overlap Bias Matching is Necessary for Point Cloud Registration](https://mlsanity.com/paper/2308.09364/) (2023)
- [DBDNet:Partial-to-Partial Point Cloud Registration with Dual Branches Decoupling](https://mlsanity.com/paper/2310.11733/) (2023)
- [3D Registration in 30 Years: A Survey](https://mlsanity.com/paper/2412.13735/) (2024)
- [Iterative Feedback Network for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2401.04357/) (2024)
- [Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2411.01870/) (2024)
- [Multiview Point Cloud Registration via Optimization in an Autoencoder Latent Space](https://mlsanity.com/paper/2504.21467/) (2025)
- [One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration](https://mlsanity.com/paper/2307.14019/) (2023)
- [Robust Partial 3D Point Cloud Registration via Confidence Estimation under Global Context](https://mlsanity.com/paper/2509.24275/) (2025)
- [Leveraging Cycle-Consistent Anchor Points for Self-Supervised RGB-D Registration](https://mlsanity.com/paper/2510.14354/) (2025)
- [From Single Scan to Sequential Consistency: A New Paradigm for LIDAR Relocalization](https://mlsanity.com/paper/2602.03198/) (2026)
- [Unsupervised Point Cloud Registration via Training-Time Semantic Guidance](https://mlsanity.com/paper/2609.15228/) (2026)

## Abstract (arXiv)

Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation. Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss, and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.