PCRMLP : A Two-Stage Network for Point Cloud Registration in Urban Scenes

Liu, Jingyang; Xu, Yuanyuan; Zhang, Lu; Sun, Lei

doi:10.20944/preprints202304.0804.v1

Cited by 3 publications

(1 citation statement)

References 29 publications

(46 reference statements)

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“…In Ref. 37, a project-aware hierarchical transformer is introduced to capture distant dependencies and filter outliers through global extraction of point features to achieve high precision and high efficiency registration. Furthermore, Ref.…”

Section: Correspondence-based Registrationmentioning

confidence: 99%

Two-stage outlier removal strategy for correspondence-based point cloud registration

Li,

Chen,

Gao

2023

J. Appl. Rem. Sens.

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.With the emergence of keypoint matching technology, the correspondence-based point cloud registration (PCR) method has gained increasing attention. However, the correspondences generated by keypoint matching technology contain extremely high outlier rates, resulting in the correspondence-based PCR method facing issues of high computational complexity and low precision registration. We propose a correspondence-based PCR method using a coarse-to-fine outlier removal strategy with O ( N ) complexity. First, we propose a coarse outlier removal module based on linearly related properties, i.e., we build a deviation matrix that can measure each correspondence deviation the degree away from the ideal inlier. The module can reduce the number of correspondences and the outlier rates. Then, we propose a fine outlier removal module that adopts each correspondence to identify outlier based on the spatial geometric mapping invariance. Finally, to increase registration accuracy, we introduce a graduated non-convexity with Tukey’s biweight method. It can avoid the solution falling into the local minimum and better reduce the influence of outliers. Experimental results show that the proposed method is robust at outlier rates above 99% and is faster than state-of-the-art methods.

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Section: Correspondence-based Registrationmentioning

confidence: 99%

Two-stage outlier removal strategy for correspondence-based point cloud registration

Li,

Chen,

Gao

2023

J. Appl. Rem. Sens.

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Kalman-Based Scene Flow Estimation for Point Cloud Densification and 3D Object Detection in Dynamic Scenes

Ding,

Zhang,

et al. 2024

Sensors

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Point cloud densification is essential for understanding the 3D environment. It provides crucial structural and semantic information for downstream tasks such as 3D object detection and tracking. However, existing registration-based methods struggle with dynamic targets due to the incompleteness and deformation of point clouds. To address this challenge, we propose a Kalman-based scene flow estimation method for point cloud densification and 3D object detection in dynamic scenes. Our method effectively tackles the issue of localization errors in scene flow estimation and enhances the accuracy and precision of shape completion. Specifically, we introduce a Kalman filter to correct the dynamic target’s position while estimating long sequence scene flow. This approach helps eliminate the cumulative localization error during the scene flow estimation process. Extended experiments on the KITTI 3D tracking dataset demonstrate that our method significantly improves the performance of LiDAR-only detectors, achieving superior results compared to the baselines.

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Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation

Kang,

Geng,

Lin

et al. 2024

Sensors

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Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points’ distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method’s geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result’s quality, our proposed method offers a solution with fewer iterations and higher accuracy.

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PCRMLP : A Two-Stage Network for Point Cloud Registration in Urban Scenes

Cited by 3 publications

References 29 publications

Two-stage outlier removal strategy for correspondence-based point cloud registration

Two-stage outlier removal strategy for correspondence-based point cloud registration

Kalman-Based Scene Flow Estimation for Point Cloud Densification and 3D Object Detection in Dynamic Scenes

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation

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