ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.
Dy- namic 3d gaussian fields for urban areas
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StreetNVS presents a multi-sensor conditioned video diffusion framework for street-view novel view synthesis that outperforms baselines with sparse LiDAR and handles extreme out-of-trajectory paths on the Waymo dataset.
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
FACT-GS allocates higher texture sampling density to high-frequency areas in 2D Gaussian Splatting through a learnable deformation field, recovering sharper details at the same parameter budget.
citing papers explorer
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ProDiG: Progressive Diffusion-Guided Gaussian Splatting for Aerial to Ground Reconstruction
ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.
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Effective Multi-sensor Conditioning for Street-view Novel-view Synthesis
StreetNVS presents a multi-sensor conditioned video diffusion framework for street-view novel view synthesis that outperforms baselines with sparse LiDAR and handles extreme out-of-trajectory paths on the Waymo dataset.
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Visually-grounded Humanoid Agents
A coupled world-agent framework uses 3D Gaussian reconstruction and first-person RGB-D perception with iterative planning to enable goal-directed, collision-avoiding humanoid behavior in novel reconstructed scenes.
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FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
FACT-GS allocates higher texture sampling density to high-frequency areas in 2D Gaussian Splatting through a learnable deformation field, recovering sharper details at the same parameter budget.