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Point cloud shape completion, which aims to reconstruct the missing regions of the incomplete point clouds with plausible shapes, is an ill-posed and challenging task that benefits many downstream 3D applications. Prior approaches achieve this goal by employing a two-stage completion framework, generating a coarse yet complete seed point cloud through an encoder-decoder network, followed by refinement and upsampling. However, the encoded features suffer from information loss of the missing portion, leading to an inability of the decoder to reconstruct seed points with detailed geometric clues. To tackle this issue, we propose a novel Orthogonal Dictionary Guided Shape Completion Network (ODGNet). The proposed ODGNet consists of a Seed Generation U-Net, which leverages multi-level feature extraction and concatenation to significantly enhance the representation capability of seed points, and Orthogonal Dictionaries that can learn shape priors from training samples and thus compensate for the information loss of the missing portions during inference. Our design is simple but to the point, extensive experiment results indicate that the proposed method can reconstruct point clouds with more details and outperform previous state-of-the-art counterparts. The implementation code is available at https://github.com/corecai163/ODGNet.
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Orthogonal array composite designs for drug combination experiments with applications for tuberculosis
Abstract The aim of this article is to provide an overview of the orthogonal array composite design (OACD) methodology, illustrate the various advantages, and provide a real‐world application. An OACD combines a two‐level factorial design with a three‐level orthogonal array and it can be used as an alternative to existing composite designs for building response surface models. We compare the ‐efficiencies of OACDs relative to the commonly used central composite design (CCD) when there are a few missing observations and demonstrate that OACDs are more robust to missing observations for two scenarios. The first scenario assumes one missing observation either from one factorial point or one additional point. The second scenario assumes two missing observations either from two factorial points or from two additional points, or from one factorial point and one additional point. Furthermore, we compare OACDs and CCDs in terms of ‐optimality for precise predictions. Lastly, a real‐world application of an OACD for a tuberculosis drug combination study is provided.
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- Award ID(s):
- 2123380
- PAR ID:
- 10368967
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Statistics in Medicine
- Volume:
- 41
- Issue:
- 17
- ISSN:
- 0277-6715
- Page Range / eLocation ID:
- p. 3380-3397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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