用3D高斯分布建模点云局部结构，实现更精确的点云上采样。
PU-Gaussian: Point Cloud Upsampling using 3D Gaussian Representation
- 以各点邻域的各向异性3D高斯分布建模几何结构
- 在局部几何域直接采样生成稠密粗点云，再通过优化提升均匀性与边缘清晰度
- 在PU1K和PUGAN数据集上达到当前最优性能，适合需要高保真重建的场景
三维传感器生成的点云通常稀疏且含噪，给需要密集高保真三维表示的任务带来挑战。以往方法多依赖隐式特征或距离函数学习，但常牺牲几何可解释性或对输入稀疏性的鲁棒性。为此，我们提出PU-Gaussian，一种新型上采样网络，通过各向异性3D高斯分布建模每个点的局部邻域，捕捉底层几何结构，并在局部几何域中直接采样生成稠密但粗糙的点云。随后的精炼网络调整粗输出，获得更均匀分布和更锐利边缘的结果。我们在PU1K和PUGAN数据集上进行大量测试，证明PU-Gaussian达到当前最优性能。代码与模型权重已公开于https://github.com/mvg-inatech/PU-Gaussian.git。
原文摘要 · Abstract (English)
Point clouds produced by 3D sensors are often sparse and noisy, posing challenges for tasks requiring dense and high-fidelity 3D representations. Prior work has explored both implicit feature-based upsampling and distance-function learning to address this, but often at the expense of geometric interpretability or robustness to input sparsity. To overcome these limitations, we propose PU-Gaussian, a novel upsampling network that models the local neighborhood around each point using anisotropic 3D Gaussian distributions. These Gaussians capture the underlying geometric structure, allowing us to perform upsampling explicitly in the local geometric domain by direct point sampling. The sampling process generates a dense, but coarse, point cloud. A subsequent refinement network adjusts the coarse output to produce a more uniform distribution and sharper edges. We perform extensive testing on the PU1K and PUGAN datasets, demonstrating that PU-Gaussian achieves state-of-the-art performance. We make code and model weights publicly available at https://github.com/mvg-inatech/PU-Gaussian.git.
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