通过微调基于数据驱动的先验，在单个带噪声点云上过拟合以推断神经符号距离函数
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based Priors
AI总结：
本文提出 LocalN2NM，通过局部统计推理在无 SDF 监督、干净点云和法线条件下微调数据驱动先验，从而在单个高噪声点云上快速过拟合学习神经 SDF，并提升重建与去噪性能。
摘要：
在许多计算机视觉应用中，从点云估计准确的符号距离函数（SDF）非常重要。最新方法通过基于数据驱动或基于过拟合的策略来学习神经 SDF。然而，这两类方法分别存在泛化能力差或收敛缓慢的问题，这限制了它们在高噪声点云等具有挑战性场景中的能力。为解决该问题，我们提出一种方法，在学习神经 SDF 时结合基于数据驱动和基于过拟合方法的优势，以实现更好的泛化、更快的推理和更高的精度。我们引入了一种新颖的局部区域统计推理算法，该算法能够在没有符号距离监督、干净点云或点法线的情况下微调基于数据驱动的先验。这有助于我们的方法从良好的初始化开始，并以更快的方式收敛到极小值。我们与最先进方法进行的数值和视觉比较表明，在广泛使用的形状和场景基准上，本方法在表面重建和点云去噪方面优于这些方法。代码可在 https://github.com/chenchao15/LocalN2NM 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
