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LSVSH描述符: 高鉴别强鲁棒的点云局部特征统计直方图

LSVSH descriptor: an local feature statistic histogram with high discrimination and strong robustness

  • 摘要: 三维局部特征描述是三维计算机视觉中的重要任务. 现实场景中包含的噪声、遮挡和杂波等干扰, 使得准确和鲁棒的三维局部特征描述仍具有很大的挑战性. 为提高特征描述的性能, 提出一种新颖的LSVSH局部特征描述符. 首先, 设计一种用于不依赖于LRA的新属性(称为曲率属性), 增强描述符对LRA误差的稳健性; 然后, 沿径向剖分局部空间, 并在每个子空间中统计3个角度属性和1个曲率属性来生成LSVSH描述符, 实现对局部曲面信息的全面稳健描述. 在B3R, U3M, U3OR和QuLD数据集上进行大量的实验. 实验结果表明: LSVSH在B3R, U3M, U3OR和QuLD数据集上的AUCpr值分别为0.95, 0.70, 0.54和0.10, 其性能优于现有的局部特征描述符; LSVSH在U3M数据集上的正确配准率和在U3OR数据集上的正确识别率分别达到70%和100%, 验证了LSVSH应用于物体配准和识别任务上的有效性.

     

    Abstract:  Three-dimensional (3-D) local feature description is an important task in 3-D computer vision. However, accurate and robust feature description is still a challenge in the presence of various nuisances (including noise, occlusion and clutter) contained in real scenes. To improve the performance of feature description, this paper proposes a novel LSVSH local feature descriptor. First, a new attribute (called curvature attribute) with independent of LRA is designed, improving the robustness of LSVSH to the error of LRA. Second, the local space is divided into several bins along the radial direction, and then three angle attributes and one curvature attribute are counted in each bin to generate the LSVSH descriptor, achieving a full and robust description of local surface information. Extensive experiments on B3R, U3M, U3OR and QuLD datasets are implemented. Results show that the AUCpr values of LSVSH on B3R, U3M, U3OR and QuLD datasets are 0.95, 0.70, 0.54 and 0.10 respectively, which are all superior to existing local feature descriptors.; LSVSH achieves 70% correct registration on U3M dataset and 100% correct recognition on U3OR dataset, verifying its effectiveness in these applications.

     

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