# OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration

arXiv: https://arxiv.org/abs/2103.00937 · HTML: https://mlsanity.com/paper/2103.00937/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [PointNetLK: Robust & Efficient Point Cloud Registration using PointNet](https://mlsanity.com/paper/1903.05711/) (2019)
- [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)
- [Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences](https://mlsanity.com/paper/2005.01014/) (2020)
- [DeepGMR: Learning Latent Gaussian Mixture Models for Registration](https://mlsanity.com/paper/2008.09088/) (2020)
- [PCRNet: Point Cloud Registration Network using PointNet Encoding](https://mlsanity.com/paper/1908.07906/) (2019)
- [SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences](https://mlsanity.com/paper/1904.03483/) (2019)

## Built on by (most cited first, 30 of 51)

- [Geometric Transformer for Fast and Robust Point Cloud Registration](https://mlsanity.com/paper/2202.06688/) (2022)
- [REGTR: End-to-end Point Cloud Correspondences with Transformers](https://mlsanity.com/paper/2203.14517/) (2022)
- [Lepard: Learning partial point cloud matching in rigid and deformable scenes](https://mlsanity.com/paper/2111.12591/) (2021)
- [GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer](https://mlsanity.com/paper/2308.03768/) (2023)
- [SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration](https://mlsanity.com/paper/2203.14453/) (2022)
- [Open-vocabulary object 6D pose estimation](https://mlsanity.com/paper/2312.00690/) (2023)
- [Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration](https://mlsanity.com/paper/2303.13290/) (2023)
- [ImLoveNet: Misaligned Image-supported Registration Network for Low-overlap Point Cloud Pairs](https://mlsanity.com/paper/2207.00826/) (2022)
- [FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration](https://mlsanity.com/paper/2106.03479/) (2021)
- [Self-Supervised Point Cloud Registration with Deep Versatile Descriptors](https://mlsanity.com/paper/2201.10034/) (2022)
- [Overlap-guided Gaussian Mixture Models for Point Cloud Registration](https://mlsanity.com/paper/2210.09836/) (2022)
- [SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks](https://mlsanity.com/paper/2403.19474/) (2024)
- [Robust Partial-to-Partial Point Cloud Registration in a Full Range](https://mlsanity.com/paper/2111.15606/) (2021)
- [WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration](https://mlsanity.com/paper/2108.02740/) (2021)
- [Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction](https://mlsanity.com/paper/2112.09385/) (2021)
- [Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes](https://mlsanity.com/paper/2404.04557/) (2024)
- [UTOPIC: Uncertainty-aware Overlap Prediction Network for Partial Point Cloud Registration](https://mlsanity.com/paper/2208.02712/) (2022)
- [Point Cloud Registration using Representative Overlapping Points](https://mlsanity.com/paper/2107.02583/) (2021)
- [Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration](https://mlsanity.com/paper/2308.05314/) (2023)
- [EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling](https://mlsanity.com/paper/2412.17249/) (2024)
- [Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration](https://mlsanity.com/paper/2311.01202/) (2023)
- [SE(3)-bi-equivariant Transformers for Point Cloud Assembly](https://mlsanity.com/paper/2407.09167/) (2024)
- [PointRegGPT: Boosting 3D Point Cloud Registration using Generative Point-Cloud Pairs for Training](https://mlsanity.com/paper/2407.14054/) (2024)
- [Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching](https://mlsanity.com/paper/2412.19328/) (2024)
- [A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation](https://mlsanity.com/paper/2302.14511/) (2023)
- [Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05234/) (2023)
- [PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration](https://mlsanity.com/paper/2312.06063/) (2023)
- [Deep Loss Convexification for Learning Iterative Models](https://mlsanity.com/paper/2411.10649/) (2024)
- [Fully-Geometric Cross-Attention for Point Cloud Registration](https://mlsanity.com/paper/2502.08285/) (2025)
- [Q-REG: End-to-End Trainable Point Cloud Registration with Surface Curvature](https://mlsanity.com/paper/2309.16023/) (2023)

## Abstract (arXiv)

Point cloud registration is a key task in many computational fields. Previous correspondence matching based methods require the inputs to have distinctive geometric structures to fit a 3D rigid transformation according to point-wise sparse feature matches. However, the accuracy of transformation heavily relies on the quality of extracted features, which are prone to errors with respect to partiality and noise. In addition, they can not utilize the geometric knowledge of all the overlapping regions. On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features. In this paper, we present OMNet, a global feature based iterative network for partial-to-partial point cloud registration. We learn overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape. Moreover, the previously used data is sampled only once from the CAD models for each object, resulting in the same point clouds for the source and reference. We propose a more practical manner of data generation where a CAD model is sampled twice for the source and reference, avoiding the previously prevalent over-fitting issue. Experimental results show that our method achieves state-of-the-art performance compared to traditional and deep learning based methods. Code is available at https://github.com/megvii-research/OMNet.