Browsing by Author "Zhang, Yuhe"

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    HDRNet: High‐Dimensional Regression Network for Point Cloud Registration
    (Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Gao, Jian; Zhang, Yuhe; Liu, Zehua; Li, Siyi; Hauser, Helwig and Alliez, Pierre
    Abstract‐3D point cloud registration is a crucial topic in the reverse engineering, computer vision and robotics fields. The core of this problem is to estimate a transformation matrix for aligning the source point cloud with a target point cloud. Several learning‐based methods have achieved a high performance. However, they are challenged with both partial overlap point clouds and multiscale point clouds, since they use the singular value decomposition (SVD) to find the rotation matrix without fully considering the scale information. Furthermore, previous networks cannot effectively handle the point clouds having large initial rotation angles, which is a common practical case. To address these problems, this paper presents a learning‐based point cloud registration network, namely HDRNet, which consists of four stages: local feature extraction, correspondence matrix estimation, feature embedding and fusion and parametric regression. HDRNet is robust to noise and large rotation angles, and can effectively handle the partial overlap and multi‐scale point clouds registration. The proposed model is trained on the ModelNet40 dataset, and compared with ICP, SICP, FGR and recent learning‐based methods (PCRNet, IDAM, RGMNet and GMCNet) under several settings, including its performance on moving to invisible objects, with higher success rates. To verify the effectiveness and generality of our model, we also further tested our model on the Stanford 3D scanning repository.
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    SGLBP: Subgraph‐based Local Binary Patterns for Feature Extraction on Point Clouds
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Guo, Bao; Zhang, Yuhe; Gao, Jian; Li, Chunhui; Hu, Yao; Hauser, Helwig and Alliez, Pierre
    Extraction for points that can outline the shape of a point cloud is an important task for point cloud processing in various applications. The topology information of the neighbourhood of a point usually contains sufficient information for detecting features, which is fully considered in this study. Therefore, a novel method for extracting feature points based on the topology information is proposed. First, an improved ‐shape technique is introduced, generating two graphs for potential feature detection and neighbourhood description, respectively. Local binary pattern (LBP) is then applied to the subgraphs, thus subgraph‐based local binary patterns (SGLBPs) are generated for encoding the topology of the neighbourhoods of points, which helps to remove non‐feature points from potential feature points. The proposed method can directly process raw point clouds and needs no prior surface reconstruction or geometric invariants computation; furthermore, the proposed method detects feature points by analysing the topologies of the neighbourhoods of points, consequently promoting the effectiveness for tiny features and the robustness to noises and non‐uniformly sampling patterns. The experimental results demonstrate that the proposed method is robust and achieves state‐of‐the‐art performance.