Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural Network

Point clouds frequently contain noise and outliers, presenting obstacles for downstream applications. In this work, we introduce a novel denoising method for point clouds. By leveraging the latent space, we explicitly un-cover noise components, allowing for the extraction of a clean latent code. This, in turn, facilitates the restoration of clean points via inverse transformation. A key component in our network is a new multi-level graph convolution network for capturing rich geometric structural features at various scales from local to global. These features are then integrated into the invertible neural network which bijectively maps the latent space, to guide the noise disentanglement process. Additionally, we employ an invertible mono-tone operator to model the transformation process, effectively enhancing the representation of integrated geometric features. This enhancement allows our network to pre-cisely differentiate between noise factors and the intrinsic clean points in the latent code by projecting them onto separate channels. Both qualitative and quantitative evaluations demonstrate that our method outperforms state-of-the-art methods at various noise levels. The source code is available at https://github.com/yanbiaol/PD-LTS.