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报告摘要: Generative models, which map a latent parameter space to instances in an ambient space, enjoy various applications in 3D Vision and related domains. A standard scheme of these models is probabilistic, which aligns the induced ambient distribution of a generative model from a prior distribution of the latent space with the empirical ambient distribution of training instances. While thi...

报告摘要: In recent years, the 3D vision community has witnessed a significant shift towards Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) as primary focal points. These innovations have fundamentally transformed how we represent 3D space and learn these representations through deep learning techniques with 2D image supervision. Existing research in NeRF and 3DGS can be broadly...