Maath Musleh, L. Muren, L. Toussaint, A. Vestergaard, E. Gröller, R. Raidou
{"title":"Uncertainty guidance in proton therapy planning visualization","authors":"Maath Musleh, L. Muren, L. Toussaint, A. Vestergaard, E. Gröller, R. Raidou","doi":"10.2139/ssrn.4263600","DOIUrl":"https://doi.org/10.2139/ssrn.4263600","url":null,"abstract":"","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"211 1","pages":"166-179"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79011910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiqiang Zhao, Xingyi Ma, Bin Hu, Qi Zhang, Mao Ye, Guoqing Zhou
{"title":"A large-scale point cloud semantic segmentation network via local dual features and global correlations","authors":"Yiqiang Zhao, Xingyi Ma, Bin Hu, Qi Zhang, Mao Ye, Guoqing Zhou","doi":"10.2139/ssrn.4226670","DOIUrl":"https://doi.org/10.2139/ssrn.4226670","url":null,"abstract":"","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"1 1","pages":"133-144"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89123111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a real-time fluid rendering method based on the screen space rendering scheme for particle-based fluid simulation. Our method applies anisotropic transformations to the point sprites to stretch the point sprites along appropriate axes, obtaining smooth fluid surfaces based on the weighted principal components analysis of the particle distribution. Then we combine the processed anisotropic point sprite information with popular screen space filters like curvature flow and narrow-range filters to process the depth information. Experiments show that the proposed method can efficiently resolve the issues of jagged edges and unevenness on the surface that existed in previous methods while preserving sharp high-frequency details
{"title":"Anisotropic screen space rendering for particle-based fluid simulation","authors":"Yanrui Xu, Yuanmu Xu, Yuege Xiong, Dou Yin, Xiaojuan Ban, Xiaokun Wang, Jian Chang, Jian Zhang","doi":"10.2139/ssrn.4217325","DOIUrl":"https://doi.org/10.2139/ssrn.4217325","url":null,"abstract":"This paper proposes a real-time fluid rendering method based on the screen space rendering scheme for particle-based fluid simulation. Our method applies anisotropic transformations to the point sprites to stretch the point sprites along appropriate axes, obtaining smooth fluid surfaces based on the weighted principal components analysis of the particle distribution. Then we combine the processed anisotropic point sprite information with popular screen space filters like curvature flow and narrow-range filters to process the depth information. Experiments show that the proposed method can efficiently resolve the issues of jagged edges and unevenness on the surface that existed in previous methods while preserving sharp high-frequency details","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"8 1","pages":"118-124"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89962543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"BrightFormer: A transformer to brighten the image","authors":"Yong Wang, Bo Li, Xi Yuan","doi":"10.2139/ssrn.4194700","DOIUrl":"https://doi.org/10.2139/ssrn.4194700","url":null,"abstract":"","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"33 1","pages":"49-57"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80805601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.48550/arXiv.2211.00519
Yanran Guan, Andrei Chubarau, Ruby Rao, D. Nowrouzezahrai
Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations commonly couple the 3D shape data with auxiliary surface-mapped image data, such as diffuse color textures and fine-scale geometric details in normal maps that typically require a mapping of the 3D surface onto a plane, i.e., a surface parameterization; implicit representations, on the other hand, cannot be easily textured due to lack of configurable surface parameterization. Inspired by this digital content authoring methodology, we design a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data. As such, our model remains compatible with existing mesh-based digital content with appearance data. Motivated by recent work that overfits compact networks to individual 3D objects, we present a new weight-encoded neural implicit representation that extends the capability of neural implicit surfaces to enable various common and important applications of texture mapping. Our method outperforms reasonable baselines and state-of-the-art alternatives.
{"title":"Learning Neural Implicit Representations with Surface Signal Parameterizations","authors":"Yanran Guan, Andrei Chubarau, Ruby Rao, D. Nowrouzezahrai","doi":"10.48550/arXiv.2211.00519","DOIUrl":"https://doi.org/10.48550/arXiv.2211.00519","url":null,"abstract":"Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations commonly couple the 3D shape data with auxiliary surface-mapped image data, such as diffuse color textures and fine-scale geometric details in normal maps that typically require a mapping of the 3D surface onto a plane, i.e., a surface parameterization; implicit representations, on the other hand, cannot be easily textured due to lack of configurable surface parameterization. Inspired by this digital content authoring methodology, we design a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data. As such, our model remains compatible with existing mesh-based digital content with appearance data. Motivated by recent work that overfits compact networks to individual 3D objects, we present a new weight-encoded neural implicit representation that extends the capability of neural implicit surfaces to enable various common and important applications of texture mapping. Our method outperforms reasonable baselines and state-of-the-art alternatives.","PeriodicalId":51003,"journal":{"name":"Computer Graphics World","volume":"39 1","pages":"257-264"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77080768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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