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.

翻译：通过采集设备或三维重建技术获取的点云常含有噪声，会干扰下游任务。本文旨在恢复含噪点云的底层表面。我们设计了一种新型模型NoiseTrans，采用Transformer编码器架构进行点云去噪。具体而言，借助Transformer核心的自注意力机制，我们获取了点状点云的结构相似性。通过将含噪点云表示为无序向量集合，我们将点云转换为点嵌入，并利用Transformer生成干净点云。为使Transformer在感知点云时保留细节，我们设计了局部点注意力机制以防止点云过度平滑。此外，我们还提出了稀疏编码方法，使Transformer能更好地感知点云的结构关系，从而提升去噪性能。实验表明，我们的模型在多种数据集和噪声环境下均优于现有最先进方法。