  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.
