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
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
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.
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AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network
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.