{"id":306419,"date":"2025-11-20T15:11:27","date_gmt":"2025-11-20T15:11:27","guid":{"rendered":"https:\/\/peraltafinancing.com\/animation\/fast-reconstruction-of-implicit-surfaces-using-convolutional-neural-networks-physics-based-animation\/"},"modified":"2025-11-20T15:11:27","modified_gmt":"2025-11-20T15:11:27","slug":"fast-reconstruction-of-implicit-surfaces-using-convolutional-neural-networks-physics-based-animation","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=306419","title":{"rendered":"Fast Reconstruction of Implicit Surfaces Using Convolutional Neural Networks \u2013 Physics-Based Animation"},"content":{"rendered":"


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Chen Zhao, Tamar Shinar, Craig Schroeder<\/p>\n

Recently, Zhao et al . [2024] proposed a new method for constructing signed distance functions from fluid simulation particles. This method was able to achieve superior surface smoothness, noise reduction, and temporal coherence compared with previous methods. One of the main limitations of the method was its relatively slow construction times, even though it utilized both the CPU and GPU. In this paper, we consider two modifications to this scheme that make the algorithm easier to optimize without introducing any perceptible changes in reconstruction quality, as illustrated in Figure 1. With these improvements, a surface can be reconstructed from a single fluid simulation with 2M particles in 2.21 seconds, compared with 72.3 seconds for the original method, resulting in a single-frame reconstruction speedup of about 33\u00d7, making the surface reconstruction fast enough for use within a simulation framework. When reconstructing surface for multiple simulation frames together, we achieve a speedup of about 5\u00d7 compared with the original. The optimized implementation will be released with the publication.<\/p>\n

Fast Reconstruction of Implicit Surfaces Using Convolutional Neural Networks<\/a><\/p>\n<\/p><\/div>\n

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Chen Zhao, Tamar Shinar, Craig Schroeder Recently, Zhao et al . [2024] proposed a new method for constructing signed distance functions from fluid simulation particles. This method was able to achieve superior surface smoothness, noise reduction, and temporal coherence compared with previous methods. One of the main limitations of the method was its relatively slow […]<\/p>\n","protected":false},"author":1,"featured_media":245146,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12035],"tags":[6119,137227,528,30922,14572,23565,18075,19534,22416],"dealstore":[],"offerexpiration":[],"class_list":["post-306419","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-animation","tag-animation","tag-convolutional","tag-fast","tag-implicit","tag-networks","tag-neural","tag-physicsbased","tag-reconstruction","tag-surfaces"],"yoast_head":"\nFast Reconstruction of Implicit Surfaces Using Convolutional Neural Networks \u2013 Physics-Based Animation - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=306419\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fast Reconstruction of Implicit Surfaces Using Convolutional Neural Networks \u2013 Physics-Based Animation - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Chen Zhao, Tamar Shinar, Craig Schroeder Recently, Zhao et al . [2024] proposed a new method for constructing signed distance functions from fluid simulation particles. This method was able to achieve superior surface smoothness, noise reduction, and temporal coherence compared with previous methods. 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