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By combining feed-forward 3D reconstruction with a geometry-aware diffusion model, Leveling3D fills in the gaps in extrapolated novel views, leveling up both 3D reconstruction and generation.
Achieve SOTA extrapolated-view LiDAR synthesis by fusing multi-frame LiDAR data and spatially-constrained dropout regularization, enabling robust autonomous driving simulation without multi-pass data.
Diffusion models can fill the depth-sensing void in endoscopies, turning unreliable sparse data into robust 3D reconstructions.
Achieve state-of-the-art surgical attention tracking with a new method that leverages temporal coherence and a large-scale benchmark dataset, enabling more robust and interpretable FoV guidance.
