A point cloud as an information-intensive 3D representation usually requires a large amount of transmission, storage and computing resources, which seriously hinder its usage in many emerging fields. In this paper, we propose a novel point cloud simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands in real-world application scenarios. The method includes point cloud pre-processing (denoising and down-sampling), AIVS-based realization for isotropic simplification and flexible simplification with intrinsic control of point distance. To demonstrate the effectiveness of the proposed AIVS-based method, we conducted extensive experiments by comparing it with several relevant point cloud simplification methods on three public datasets, including Stanford, SHREC, and RGB-D scene models. The experimental results indicate that AIVS has great advantages over peers in terms of moving least squares (MLS) surface approximation quality, curvature-sensitive sampling, sharp-feature keeping and processing speed. The source code of the proposed method is publicly available. ( https://github.com/vvvwo/AIVS-project ).

中文翻译：

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用于点云简化的近似固有体素结构


点云作为信息密集型的3D表示通常需要大量的传输、存储和计算资源，这严重阻碍了其在许多新兴领域的使用。在本文中，我们提出了一种新颖的点云简化方法——近似固有体素结构（AIVS），以满足实际应用场景中的多样化需求。该方法包括点云预处理（去噪和下采样）、基于AIVS的各向同性简化的实现以及点距离内在控制的灵活简化。为了证明所提出的基于 AIVS 的方法的有效性，我们在三个公共数据集（包括斯坦福、SHREC 和 RGB-D 场景模型）上将其与几种相关的点云简化方法进行了比较，进行了广泛的实验。实验结果表明，AIVS在移动最小二乘（MLS）表面逼近质量、曲率敏感采样、锐特征保持和处理速度方面比同行具有很大优势。所提出方法的源代码是公开的。 （https://github.com/vvvwo/AIVS-project）。