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.
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.
In this paper, we present the details of our team WrightEagle@Home's approaches. Our KeJia robot won the RoboCup@Home competition 2014 and accomplished two tests which have never been fully solved before. Our work covers research issues ranging from hardware, perception and high-level cognitive functions. All these techniques and the whole robot system have been exhaustively tested in the competition and have shown good robustness.
Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state‐of‐the‐art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness.
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