Robust Point Cloud Registration Framework Based on Deep Graph Matching

• URL: http://arxiv.org/abs/2103.04256v1
• Date: Sun, 7 Mar 2021 04:20:29 GMT
• Title: Robust Point Cloud Registration Framework Based on Deep Graph Matching
• Authors: Kexue Fu and Shaolei Liu and Xiaoyuan Luo and Manning Wang
• Abstract summary: 3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matchingbased framework for point cloud registration.
• Score: 5.865029600972316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D point cloud registration is a fundamental problem in computer vision and robotics. There has been extensive research in this area, but existing methods meet great challenges in situations with a large proportion of outliers and time constraints, but without good transformation initialization. Recently, a series of learning-based algorithms have been introduced and show advantages in speed. Many of them are based on correspondences between the two point clouds, so they do not rely on transformation initialization. However, these learning-based methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matchingbased framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by singular value decomposition. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on registering clean, noisy, partial-to-partial and unseen category point clouds show that the proposed method achieves state-of-the-art performance. The code will be made publicly available at https://github.com/fukexue/RGM.

Related papers

• SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks [14.548198408544032]
  We treat 3D scene graph alignment as a partial graph-matching problem and propose to solve it with a graph neural network. We reuse the geometric features learned by a point cloud registration method and associate the clustered point-level geometric features with the node-level semantic feature. We propose a point-matching rescoring method, that uses the node-wise alignment of the 3D scene graph to reweight the matching candidates from a pre-trained point cloud registration method.
  arXiv  Detail & Related papers  (2024-03-28T15:01:58Z)
• A Strong Baseline for Point Cloud Registration via Direct Superpoints Matching [7.308509114539376]
  We propose a simple and effective baseline to find correspondences of superpoints in a global matching manner. Our simple yet effective baseline shows comparable or even better results than state-of-the-art methods on three datasets.
  arXiv  Detail & Related papers  (2023-07-03T21:33:40Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version) [13.286247750893681]
  3D point cloud registration is a fundamental problem in computer vision and robotics. We propose a novel deep graph matching-based framework for point cloud registration.
  arXiv  Detail & Related papers  (2022-11-09T06:05:25Z)
• REGTR: End-to-end Point Cloud Correspondences with Transformers [79.52112840465558]
  We conjecture that attention mechanisms can replace the role of explicit feature matching and RANSAC. We propose an end-to-end framework to directly predict the final set of correspondences. Our approach achieves state-of-the-art performance on 3DMatch and ModelNet benchmarks.
  arXiv  Detail & Related papers  (2022-03-28T06:01:00Z)
• Geometric Transformer for Fast and Robust Point Cloud Registration [53.10568889775553]
  We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios. We propose Geometric Transformer to learn geometric feature for robust superpoint matching.
  arXiv  Detail & Related papers  (2022-02-14T13:26:09Z)
• GenReg: Deep Generative Method for Fast Point Cloud Registration [18.66568286698704]
  We propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration. The experiments on both ModelNet40 and 7Scene datasets demonstrate that the proposed algorithm achieves state-of-the-art accuracy and efficiency.
  arXiv  Detail & Related papers  (2021-11-23T10:52:09Z)
• DeepBBS: Deep Best Buddies for Point Cloud Registration [55.12101890792121]
  DeepBBS is a novel method for learning a representation that takes into account the best buddy distance between points during training. Our experiments show improved performance compared to previous methods.
  arXiv  Detail & Related papers  (2021-10-06T19:00:07Z)
• Point Cloud Registration using Representative Overlapping Points [10.843159482657303]
  We propose ROPNet, a new deep learning model using Representative Overlapping Points with discriminative features for registration. Specifically, we propose a context-guided module which uses an encoder to extract global features for predicting point overlap score. Experiments over ModelNet40 using noisy and partially overlapping point clouds show that the proposed method outperforms traditional and learning-based methods.
  arXiv  Detail & Related papers  (2021-07-06T12:52:22Z)
• Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking [58.30147362745852]
  Data association across frames is at the core of Multiple Object Tracking (MOT) task. Existing methods mostly ignore the context information among tracklets and intra-frame detections. We propose a novel learnable graph matching method to address these issues.
  arXiv  Detail & Related papers  (2021-03-30T08:58:45Z)
• RPM-Net: Robust Point Matching using Learned Features [79.52112840465558]
  RPM-Net is a less sensitive and more robust deep learning-based approach for rigid point cloud registration. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility.
  arXiv  Detail & Related papers  (2020-03-30T13:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.