仅用一个带噪点云，快速准确重建表面形状。
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors
- 通过局部统计推理微调数据驱动先验，无需距离标签或法向量。
- 在噪声点云上收敛更快，重建精度显著优于当前最优方法。
- 适合点云去噪与三维形状重建任务，尤其适用于复杂噪声场景。
在许多计算机视觉应用中，从点云估计精确的符号距离函数（SDF）至关重要。现有方法分为基于数据驱动和过拟合两类，但前者泛化能力差，后者收敛慢，难以应对高噪声点云等挑战。为此，我们提出一种新方法，融合两类策略的优势，实现更好的泛化性、更快的推理速度和更高的准确性。我们在局部区域引入一种新颖的统计推理算法，无需符号距离监督、干净点云或点法向量即可微调数据驱动先验，从而获得良好初始化并快速收敛至最优解。在广泛使用的形状与场景基准上的数值与视觉对比表明，我们的方法在表面重建和点云去噪方面均优于现有最先进方法。代码已公开于 https://github.com/chenchao15/LocalN2NM。
原文摘要 · Abstract (English)
It is important to estimate an accurate signed distance function (SDF) from a point cloud in many computer vision applications. The latest methods learn neural SDFs using either a data-driven based or an overfitting-based strategy. However, these two kinds of methods are with either poor generalization or slow convergence, which limits their capability under challenging scenarios like highly noisy point clouds. To resolve this issue, we propose a method to promote pros of both data-driven based and overfitting-based methods for better generalization, faster inference, and higher accuracy in learning neural SDFs. We introduce a novel statistical reasoning algorithm in local regions which is able to finetune data-driven based priors without signed distance supervision, clean point cloud, or point normals. This helps our method start with a good initialization, and converge to a minimum in a much faster way. Our numerical and visual comparisons with the state-of-the-art methods show our superiority over these methods in surface reconstruction and point cloud denoising on widely used shape and scene benchmarks. The code is available at https://github.com/chenchao15/LocalN2NM.
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