A point cloud denoising network based on manifold in an unknown noisy environment

Acquiring original point cloud data from 3D sensors is easily affected by the environment and inherent limitations of the sensor, which inevitably contain noise and outliers. The irregularity and disorder of point clouds make denoising challenging. Traditional point cloud denoising methods heavily rely on geometric or noise priors. Recently, a deep learning denoising method based on gradient estimation has been proposed. Although it achieves the best denoising performance under Gaussian noise, the gradient can fluctuate under unknown noise, leading to decreased denoising performance. To address the problem that the above two algorithms have reduced denoising ability when facing unknown scenes, this paper proposes a denoising network based on manifold features of point clouds for unknown scenes. Specifically, the neural network consists of two key components: one is the manifold feature extraction module, which extracts manifold features and ensures accurate estimation of noise offset the other is the manifold denoising module, which can predict the noise offset of different disturbance points in the manifold by taking manifold features as input. Experiments show that our proposed method outperforms existing denoising methods on various noise model datasets and real outdoor datasets.