Multi-Frame Sparse Convolutional Learning for Point Cloud Color Denoising

Noise interference often occurs, during the collection of point cloud data, which can significantly affect the original color characteristics of the data. Currently, point cloud color denoising methods are usually based on graph structures or filters, whose denoising effects are generally influenced by the construction of the graph or the choice of filter. Different from them, this paper proposes a deep learning network for point cloud color denoising that combines an implicit neural autoencoder with a multi-frame sparse convolutional learning. We propose a novel sparse feature extraction module based on sparse convolution. The use of sparse convolution allows only processing the features at the effective positions of the point cloud, significantly improving computational efficiency. Moreover, considering that valuable information can gradually be diluted through sparse convolution, a pretrained implicit neural autoencoder is employed to map the input point cloud features to a more informative high-dimensional space, allowing for more thorough network learning. Furthermore, we propose a new method for processing point cloud data that compacts the point cloud while retaining all its color features. Finally, to further utilize the spatiotemporal information of the point cloud, a multi-frame fusion algorithm is also proposed. Experimental results show that compared with state-of-the-art algorithms, our proposed method achieves better denoising performance where the average PSNR of denoised point cloud is improved up to 1.14 dB.