SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation
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DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.
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SV-GS: Sparse View 4D Reconstruction with Skeleton-Driven Gaussian Splatting
SV-GS estimates a time-dependent skeleton pose plus fine deformations to enable 4D Gaussian splatting from sparse views, outperforming prior sparse methods by up to 34% PSNR on synthetic data and matching dense monocular baselines on real data with far fewer frames.
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From Drops to Grid: Noise-Aware Spatio-Temporal Neural Process for Rainfall Estimation
DropsToGrid is a spatio-temporal neural process that integrates temporal sequences from noisy irregular stations with spatial radar context to produce dense stochastic rainfall fields with calibrated uncertainty, outperforming baselines even with few stations or across regions.