  Point clouds obtained from capture devices or 3D reconstruction techniques
are often noisy and interfere with downstream tasks. The paper aims to recover
the underlying surface of noisy point clouds. We design a novel model,
NoiseTrans, which uses transformer encoder architecture for point cloud
denoising. Specifically, we obtain structural similarity of point-based point
clouds with the assistance of the transformer's core self-attention mechanism.
By expressing the noisy point cloud as a set of unordered vectors, we convert
point clouds into point embeddings and employ Transformer to generate clean
point clouds. To make the Transformer preserve details when sensing the point
cloud, we design the Local Point Attention to prevent the point cloud from
being over-smooth. In addition, we also propose sparse encoding, which enables
the Transformer to better perceive the structural relationships of the point
cloud and improve the denoising performance. Experiments show that our model
outperforms state-of-the-art methods in various datasets and noise
environments.
