In recent years, point cloud normal estimation, as a classical and
foundational algorithm, has garnered extensive attention in the field of 3D
geometric processing. Despite the remarkable performance achieved by current
Neural Network-based methods, their robustness is still influenced by the
quality of training data and the models' performance. In this study, we
designed a fundamental framework for normal estimation, enhancing existing
model through the incorporation of global information and various constraint
mechanisms. Additionally, we employed a confidence-based strategy to select the
reasonable samples for fair and robust network training. The introduced sample
confidence can be integrated into the loss function to balance the influence of
different samples on model training. Finally, we utilized existing orientation
methods to correct estimated non-oriented normals, achieving state-of-the-art
performance in both oriented and non-oriented tasks. Extensive experimental
results demonstrate that our method works well on the widely used benchmarks.

通过引入全局信息和各种约束机制，我们设计了一个基础框架来增强现有模型，同时采用基于置信度的策略选择合理样本进行公平且鲁棒的网络训练，并利用现有的定向方法纠正估计的非定向法线，在定向和非定向任务中实现了最先进的性能。广泛的实验结果证明了我们的方法对广泛使用的基准测试数据集表现良好。