VolFill uses a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into latent space and a latent Diffusion Transformer to denoise complete scenes, conditioned on geometry foundation models, outperforming baselines on SCRREAM and NRGB-D datasets.
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3 Pith papers cite this work. Polarity classification is still indexing.
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DreamEdit3D learns separate token embeddings for segmented object components via two-phase multi-view optimization to enable text-guided 3D editing with consistent image generation and mesh reconstruction.
CLEAR-NeRF augments standard NeRF with four targeted components for superior photorealism and metric accuracy in multi-ROI unbounded scenes compared to baseline NeRF and SfM-MVS methods.
citing papers explorer
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VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching
VolFill uses a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into latent space and a latent Diffusion Transformer to denoise complete scenes, conditioned on geometry foundation models, outperforming baselines on SCRREAM and NRGB-D datasets.
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DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
DreamEdit3D learns separate token embeddings for segmented object components via two-phase multi-view optimization to enable text-guided 3D editing with consistent image generation and mesh reconstruction.
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CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenes
CLEAR-NeRF augments standard NeRF with four targeted components for superior photorealism and metric accuracy in multi-ROI unbounded scenes compared to baseline NeRF and SfM-MVS methods.