Point cloud is a set of three-dimensional points in arbitrary order, which is a popular representation of 3D scene in autonomous navigation and immersive applications in recent years. Compression becomes an inevitable issue due to the huge data volume of point cloud. In order to effectively compress attributes of those points, proper reordering is important. The existing voxel-based point cloud attributes compression scheme uses a naive scan for points reordering. In this paper, we theoretically analyzed 3C properties of point cloud, i.e., Compactness, Clustering and Correlation, of different scan-orders defined by different space filling curves and disclosed that the Hilbert curve can provide the best spatial correlation preservation compared with Z-order and Gray-coded curves. It is also statistically verified that the Hilbert curve always has the best ability of attributes correlation preservation for point clouds with different sparsity. We also proposed a fast and iterative Hilbert address code generation method to implement points reordering. The Hilbert scan-order could be combined with various point cloud attribute coding methods. Experiments show that the correlation preservation feature of the proposed scan-order can bring us 6.1% and 6.5% coding gain for prediction and transform coding, respectively.

The reconstruction of gene regulatory network (GRN) is of significance in system biology. In recent years, benefiting from the advances of deep learning technologies, image-based gene expression data, which contains spatial expression patterns, has become a new resource in network inference. Most of the existing image-based GRN inference models are based on unsupervised models, due to the lack of labeled data. And a few methods employ supervised learning models, whose performance is limited by the scale of training data.In this study, in order to predict the gene regulatory network of the eye development of Drosophila embryos, we develop a weakly supervised learning method. We generate image triplets of genes according to their orientation and developing stage. Then we build a deep convolutional neural network, using triplet loss to train a siamese network and extract the relationship between genes. The new method achieves promising results in the prediction of gene regulatory relationship in the eye development of Drosophila with a total accuracy of over 72%.