通过高斯分布建模局部流形，提升稀疏非均匀点云的上采样质量
Non-uniform Point Cloud Upsampling via Local Manifold Distribution
- 用高斯函数迭代优化局部流形表示，显式建模点云分布特性
- 在多个数据集上生成更均匀、高质量的稠密点云，优于当前最优方法
- 适合处理稀疏非均匀点云场景，尤其对几何细节保留要求高的任务
现有基于学习的点云上采样方法常忽略点云内在的数据分布特性，导致在处理稀疏且非均匀点云时效果不佳。本文提出一种新方法，从流形分布角度施加约束。利用高斯函数强大的拟合能力，网络迭代优化高斯分量及其权重，精确表示局部流形。通过高斯函数的概率分布特性，构建统一的统计流形，对点云施加分布约束。在多个数据集上的实验结果表明，该方法在处理稀疏非均匀输入时，生成的稠密点云质量更高、分布更均匀，优于当前最先进的点云上采样技术。
原文摘要 · Abstract (English)
Existing learning-based point cloud upsampling methods often overlook the intrinsic data distribution charac?teristics of point clouds, leading to suboptimal results when handling sparse and non-uniform point clouds. We propose a novel approach to point cloud upsampling by imposing constraints from the perspective of manifold distributions. Leveraging the strong fitting capability of Gaussian functions, our method employs a network to iteratively optimize Gaussian components and their weights, accurately representing local manifolds. By utilizing the probabilistic distribution properties of Gaussian functions, we construct a unified statistical manifold to impose distribution constraints on the point cloud. Experimental results on multiple datasets demonstrate that our method generates higher-quality and more uniformly distributed dense point clouds when processing sparse and non-uniform inputs, outperforming state-of-the-art point cloud upsampling techniques.
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