A differentiable framework learns view-dependent 2D kernels from 3D ellipsoid primitives and latent vectors via projection and decoder networks for improved novel view synthesis.
InSIGGRAPH Asia 2024 Conference Papers
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A heterogeneous graph attention Q-network is introduced for AISC deployment that reduces completion time while improving load balance and energy use in dynamic UMEC networks.
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Learning View-Dependent Splatting Kernels
A differentiable framework learns view-dependent 2D kernels from 3D ellipsoid primitives and latent vectors via projection and decoder networks for improved novel view synthesis.