3D point cloud denoising method based on global feature guidance

Raw point cloud (PC) data acquired by 3D sensors or reconstruction algorithms inevitably contain noise and outliers, which can seriously impact downstream tasks, such as surface reconstruction and target detection. To address this problem, this paper proposes an innovative denoising method including the adaptive feature extraction (AFE) module, the gradient field estimation (GFE) module, and the further accelerated gradient ascent (FAGA) module. The method is based on considering the noisy PC as a convolutional distribution of clean PC and noise, and denoising is achieved by updating the positions of the points through gradient ascent iterations. Specifically, for a given noisy PC as input, we first extract global features in the AFE module, which are used as conditions to dynamically guide the extraction of local and non-local features to achieve adaptive feature acquisition. Next, these adaptive features are used as input to the GFE module to estimate the gradient field of the PCs and combined with our proposed FAGA module for denoising operations. Extensive qualitative and quantitative experiments are conducted on synthetic and natural PC datasets, and the results show that the proposed method exhibits superior performance relative to previous state-of-the-art methods.