  In recent years, deep learning-based point cloud normal estimation has made
great progress. However, existing methods mainly rely on the PCPNet dataset,
leading to overfitting. In addition, the correlation between point clouds with
different noise scales remains unexplored, resulting in poor performance in
cross-domain scenarios. In this paper, we explore the consistency of intrinsic
features learned from clean and noisy point clouds using an Asymmetric Siamese
Network architecture. By applying reasonable constraints between features
extracted from different branches, we enhance the quality of normal estimation.
Moreover, we introduce a novel multi-view normal estimation dataset that
includes a larger variety of shapes with different noise levels. Evaluation of
existing methods on this new dataset reveals their inability to adapt to
different types of shapes, indicating a degree of overfitting. Extensive
experiments show that the proposed dataset poses significant challenges for
point cloud normal estimation and that our feature constraint mechanism
effectively improves upon existing methods and reduces overfitting in current
architectures.
