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FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
We propose FrePolad: frequency-rectified point latent diffusion, a point cloud generation pipeline integrating a variational autoencoder (VAE) with a denoising diffusion probabilistic model (DDPM) for the latent distribution.
HyperTime: Implicit Neural Representation for Time Series
We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data.
Neural BRDF Representation and Importance Sampling
Additionally, we propose a novel approach to make our representation amenable to importance sampling: rather than inverting the trained networks, we learn to encode them in a more compact embedding that can be mapped to parameters of an analytic BRDF for which importance sampling is known.
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