Abstract. Depth scans acquired from different views may contain nuisances such as noise, occlusion, and varying point density. We propose a novel Signature of Geometric Centroids descriptor, supporting direct shape matching on the scans, without requiring any preprocessing such as scan denoising or converting into a mesh. First, we construct the descriptor by voxelizing the local shape within a uniquely defined local reference frame and concatenating geometric centroid and point density features extracted from each voxel. Second, we compare two descriptors by employing only corresponding voxels that are both non-empty, thus supporting matching incomplete local shape such as those close to scan boundary. Third, we propose a descriptor saliency measure and compute it from a descriptor-graph to improve shape matching performance. We demonstrate the descriptor's robustness and effectiveness for shape matching by comparing it with three state-of-the-art descriptors, and applying it to object/scene reconstruction and 3D object recognition.

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Backgrounds: An ongoing outbreak of novel coronavirus pneumonia (Covid-19) hit Wuhan and hundreds of cities, 29 territories globally. We present a method for scale estimation in dynamic while most of the researchers used static parameters. Methods: We use historical data and the SEIR model for important parameters assumption. And according to the timeline, we use dynamic parameters for infection topology network building. Also, the migration data is used for the Non-Wuhan area estimation which can be cross-validated for the Wuhan model. All data are from the public. Results: The estimated number of infections is 61,596 (95%CI: 58,344.02-64,847.98) by 25 Jan in Wuhan. And the estimation number of the imported cases from Wuhan of Guangzhou was 170 (95%CI: 161.27-179.26), infection scale in Guangzhou is 315 (95%CI: 109.20-520.79), while the imported cases are 168 and the scale of the infection is 339 published by the authority. Conclusions: Using dynamic network models and dynamic parameters for different time periods is an effective way of infection scale modeling.

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