Deep Learning Improvements for Sparse Spatial Field Reconstruction

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Bibliographic Details
Main Authors:	Sunderhaft, Robert, Frank, Logan, Davis, Jim
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent
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SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction
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MAGIC: Near-Optimal Data Attribution for Deep Learning
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R-Meshfusion: Reinforcement Learning Powered Sparse-View Mesh Reconstruction with Diffusion Priors
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Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
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Deep Spectral Improvement for Unsupervised Image Instance Segmentation
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SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction
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Sparse2DGS: Geometry-Prioritized Gaussian Splatting for Surface Reconstruction from Sparse Views
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SparseRecon: Neural Implicit Surface Reconstruction from Sparse Views with Feature and Depth Consistencies
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Sparse4DGS: 4D Gaussian Splatting for Sparse-Frame Dynamic Scene Reconstruction
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Spurfies: Sparse Surface Reconstruction using Local Geometry Priors
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Physics-Informed Representation Alignment for Sparse Radio-Map Reconstruction
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DICE: Diffusion Consensus Equilibrium for Sparse-view CT Reconstruction
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Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction
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GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction
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Sparse Voxels Rasterization: Real-time High-fidelity Radiance Field Rendering
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