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- W4387364120 abstract "Synthesis of new views in dynamic 3D scenes is a challenging task in 3D vision. However, most current approaches rely on radiance fields built upon multi-view camera systems for dynamic scenes, which are expensive and time-consuming, and depend on implicit representations based on Neural Radiance Fields . In this study, we introduced a new representation method for dynamic scenes called GsNeRF, which allows for fast reconstruction of objects under motion by moving a single camera in the scene, and it can render high-quality views from arbitrary time frames and camera poses.We utilize five grids to represent the dynamic scene, employing tensor decomposition for each grid to reduce storage space usage. Since the primary task of the entire model is to optimize these planes, we enforce spatio-temporal continuity of these planes through a smoothness loss. GsNeRF is combined with a miniature MLP to regress color outputs and trained using volume rendering. Through testing on a series of synthetic and real datasets, our method reduces the training time by over 100 times compared to implicit methods and achieves better rendering quality compared to explicit methods. Our approach achieves a balance between memory usage, speed, and quality overall." @default.
- W4387364120 created "2023-10-06" @default.
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- W4387364120 date "2023-11-01" @default.
- W4387364120 modified "2023-10-18" @default.
- W4387364120 title "GsNeRF: Fast novel view synthesis of dynamic radiance fields" @default.
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- W4387364120 doi "https://doi.org/10.1016/j.cag.2023.10.002" @default.
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