  We propose the use of a Transformer to accurately predict normals from point
clouds with noise and density variations. Previous learning-based methods
utilize PointNet variants to explicitly extract multi-scale features at
different input scales, then focus on a surface fitting method by which local
point cloud neighborhoods are fitted to a geometric surface approximated by
either a polynomial function or a multi-layer perceptron (MLP). However,
fitting surfaces to fixed-order polynomial functions can suffer from
overfitting or underfitting, and learning MLP-represented hyper-surfaces
requires pre-generated per-point weights. To avoid these limitations, we first
unify the design choices in previous works and then propose a simplified
Transformer-based model to extract richer and more robust geometric features
for the surface normal estimation task. Through extensive experiments, we
demonstrate that our Transformer-based method achieves state-of-the-art
performance on both the synthetic shape dataset PCPNet, and the real-world
indoor scene dataset SceneNN, exhibiting more noise-resilient behavior and
significantly faster inference. Most importantly, we demonstrate that the
sophisticated hand-designed modules in existing works are not necessary to
excel at the task of surface normal estimation.
