Proceedings of the Workshop on 3D Geometry Generation for Scientific Computing.

Marissa Ramirez de Chanlatte, Phillip Colella,Trevor Darrell,Alexandra Carlson,Peter H. N. de With,Huayu Deng,Shanyan Guan,James Hays,Tim Houben,Thomas Huisman,Nikita Jaipuria,Hans Johansen, Shuja Khalid,Akshay Krishnan,Chuming Li,Maxim Pisarenco,Amit Raj, Frank Rudzicz,Tim J. Schoonbeek, Sandhya Sridhar,Nathan Tseng,Fons van der Sommen, Chen Wang,Yunbo Wang, Tong Wu,Xiaokang Yang,Jiawei Yao, Derek Young, Xianling Zhang

IEEE/CVF Winter Conference on Applications of Computer Vision（2024）

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摘要

High-fidelity 3D geometries of the natural and built world around us are an essential part of answering some of the most pressing scientific questions of our day. Through advances in deep learning, computer vision, and artificial intelligence more broadly, much progress has been made in reconstructing real geometries from images and/or sparse data, but these methods are just beginning to be applied to scientific problems. On January 7th, 2024 we bring to-gether researchers from computer vision, applied mathe-matics, and several scientific disciplines, to discuss the state of the art in 3D geometry generation and how it can be applied to open problems in science. This paper summarizes a selection of the talks and papers that have been accepted.

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关键词

Computer Science,3D Geometry,Workshop Process,Geometry Generation,Deep Learning,Field Of Science,Computer Vision,Sparse Data,Advances In Deep Learning,Scanning Electron Microscopy,Simulated Data,Fluid Dynamics,Partial Differential Equations,Aerial Images,Effect Of Particles,Domain Adaptation,Semiconductor Devices,Photogrammetry,Surface Reconstruction,Dynamic Scenes,Neural Field,Dynamic Datasets,Tree Detection,Multi-view Stereo,Static Scenes

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