论文标题
基于梯度的点云与均匀性
Gradient-based Point Cloud Denoising with Uniformity
论文作者
论文摘要
由深度传感器捕获的点云通常被噪音污染，阻碍了进一步的分析和应用。在本文中，我们强调了点分布均匀性对下游任务的重要性。我们证明了现有基于梯度的DeNoiser产生的点云尽管取得了有希望的定量结果，但仍缺乏统一性。为此，我们提出了GPCD ++，这是一个基于梯度的DeNoiser，其超轻质网络名为UNINET，以解决均匀性。与以前的最先进方法相比，我们的方法不仅会产生竞争性甚至更好地降解结果，而且还可以显着提高统一性，从而在很大程度上受益于诸如表面重建之类的应用。
Point clouds captured by depth sensors are often contaminated by noises, obstructing further analysis and applications. In this paper, we emphasize the importance of point distribution uniformity to downstream tasks. We demonstrate that point clouds produced by existing gradient-based denoisers lack uniformity despite having achieved promising quantitative results. To this end, we propose GPCD++, a gradient-based denoiser with an ultra-lightweight network named UniNet to address uniformity. Compared with previous state-of-the-art methods, our approach not only generates competitive or even better denoising results, but also significantly improves uniformity which largely benefits applications such as surface reconstruction.
