VOID: 3D object recognition based on voxelization in invariant distance space

Yang, Jiaqi; Fan, Shichao; Huang, Zhiqiang; Quan, Siwen; Wang, Wei; Zhang, Yanning

doi:10.1007/s00371-022-02514-1

Cited by 8 publications

(3 citation statements)

References 52 publications

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“…Using this transformation matrix the model P s can be identified from the scene P t . Following [68], we let d res be 0.75 pr and t overlap be 0.04, or let d res be 1.5 pr and t overlap be 0.2.…”

Section: Methodsmentioning

confidence: 99%

Mutual Voting for Ranking 3D Correspondences

Yang

Fan

Ren

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Consistent correspondences between point clouds are vital to 3D vision tasks such as registration and recognition. In this paper, we present a mutual voting method for ranking 3D correspondences. The key insight is to achieve reliable scoring results for correspondences by refining both voters and candidates in a mutual voting scheme. First, a graph is constructed for the initial correspondence set with the pairwise compatibility constraint. Second, nodal clustering coefficients are introduced to preliminarily remove a portion of outliers and speed up the following voting process. Third, we model nodes and edges in the graph as candidates and voters, respectively. Mutual voting is then performed in the graph to score correspondences. Finally, the correspondences are ranked based on the voting scores and top-ranked ones are identified as inliers. Feature matching, 3D point cloud registration, and 3D object recognition experiments on various datasets with different nuisances and modalities verify that MV is robust to heavy outliers under different challenging settings, and can significantly boost 3D point cloud registration and 3D object recognition performance. Code will be available at: https://github.com/NWPU-YJQ-3DV/2022 Mutual Voting.

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Section: Methodsmentioning

confidence: 99%

Mutual Voting for Ranking 3D Correspondences

Yang

Fan

Ren

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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show abstract

“…The point cloud data are noisefree, with a resolution of approximately 0.5 mm. The B3R dataset [23] includes six models and 18 scenes from the Stanford 3D Scanning Repository. These 18 scenes are generated by applying random transformations to the six models and adding Gaussian noise, with the values of 0.1, 0.3, and 0.5 milliradians.…”

Section: Datasetsmentioning

confidence: 99%

Point Cloud Registration Based on Local Variation of Surface Keypoints

Zhu,

Huang,

Yue

et al. 2023

Electronics

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Keypoint detection plays a pivotal role in three-dimensional computer vision, with widespread applications in improving registration precision and efficiency. However, current keypoint detection methods often suffer from poor robustness and low discriminability. In this study, a novel keypoint detection approach based on the local variation of surface (LVS) is proposed. The LVS keypoint detection method comprises three main steps. Firstly, the surface variation index for each point is calculated using the local coordinate system. Subsequently, points with a surface variation index lower than the local average are identified as initial keypoints. Lastly, the final keypoints are determined by selecting the minimum value within the neighborhood from the initial keypoints. Additionally, a sampling consensus correspondence estimation algorithm based on geometric constraints (SAC-GC) for efficient and robust estimation of optimal transformations in correspondences is proposed. By combining LVS and SAC-GC, we propose a coarse-to-fine point cloud registration algorithm. Experimental results on four public datasets demonstrate that the LVS keypoint detection algorithm offers improved repeatability and robustness, particularly when dealing with noisy, occluded, or cluttered point clouds. The proposed coarse-to-fine point cloud registration algorithm also exhibits enhanced robustness and computational efficiency.

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“…Method: Feature extraction [10][11][12] and representation is very important and crucial for object recognition. Commonly used features include textural features [13], colour features [14], spatial relationship features [15] and shape features. Among them, shape is the most direct and important visual feature.…”

mentioning

confidence: 99%

Object recognition based on shape interest points descriptor

Zhang,

2024

Electronics Letters

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Considering the defect of object recognition with single global feature or local feature, in this letter the authors propose a shape interest points descriptor (SIPD) for object recognition. Particularly, the authors extract the shape features by the improved HU moments and then interest points by Speeded up Robust Feature (SURF). Object recognition is carried out by similarity measure. Because the influence of the improved HU moments and SURF for object recognition is different, the two different similarity measures are fused effectively by using different weight factors. Experimental results show that the author’ proposed method is effective and robust to the changes of scale, viewing angle, illumination and noise when compared with the other representative methods.

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VOID: 3D object recognition based on voxelization in invariant distance space

Cited by 8 publications

References 52 publications

Mutual Voting for Ranking 3D Correspondences

Mutual Voting for Ranking 3D Correspondences

Point Cloud Registration Based on Local Variation of Surface Keypoints

Object recognition based on shape interest points descriptor

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