English(EN) PointNDF: Cascaded Neural Displacement Fields for Point Cloud Denoising

PointNDF框架通过级联神经场增强点云去噪

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员推出PointNDF，一个新颖的框架，通过整合块级几何上下文和局部位移预测来增强点云去噪。该方法利用Point Transformer V3提取多尺度几何信息，然后由神经位移场用于预测有界向量位移和尺度因子，以提高点均匀性。该框架采用三个级联阶段进行渐进式细化，在PU-Net和ModelNet10等标准基准上展示了具有竞争力的去噪精度和高效推理。 AI

影响引入了一种改进3D点云数据质量和均匀性的新方法，可能有利于3D重建和计算机图形学中的应用。

排序理由该条目描述了一篇关于点云去噪新方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Michael Y. Wang, Pingping Cai · 2026-11-05 05:00

PointNDF：用于点云去噪的级联神经位移场

arXiv:2611.05411v1 Announce Type: new Abstract: Point cloud denoising aims to recover clean geometry from noisy 3D observations while preserving both fine surface details and the underlying spatial distribution of the points. Many learning-based methods adopt a two-stage pipeline…

本文如何被排名

报道来源 [1]

PointNDF：用于点云去噪的级联神经位移场

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