PAGaS refines multi-view stereo depths by optimizing 1DoF Gaussians whose positions and sizes are fixed by back-projected pixel volumes, producing detailed depth maps that outperform reference baselines on 3D reconstruction benchmarks.
Mvsformer++: Revealing the devil in transformer’s details for multi-view stereo
5 Pith papers cite this work. Polarity classification is still indexing.
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Presents a scene-adaptive 3D human animation method using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human trajectories and camera views, reporting gains on two benchmarks.
GeoQuery replaces corrupted rendering features with geometry-aligned proxy queries and restricts cross-view attention to local windows, enabling robust diffusion-based refinement under extreme view sparsity.
Procedural rules with NURBS generate MVS training data that outperforms same-scale manual curation and matches or exceeds larger manual datasets.
A technique reconstructs large urban areas from sparse extreme off-nadir satellite images by modeling geometry as a Z-monotonic 2.5D height map SDF and applying a generative network to restore plausible textures on the resulting mesh.
citing papers explorer
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PAGaS: Pixel-Aligned 1DoF Gaussian Splatting for Depth Refinement
PAGaS refines multi-view stereo depths by optimizing 1DoF Gaussians whose positions and sizes are fixed by back-projected pixel volumes, producing detailed depth maps that outperform reference baselines on 3D reconstruction benchmarks.
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3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement
Presents a scene-adaptive 3D human animation method using ground-adaptive motion retargeting and viewpoint-adaptive latent fusion to control human trajectories and camera views, reporting gains on two benchmarks.
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GeoQuery: Geometry-Query Diffusion for Sparse-View Reconstruction
GeoQuery replaces corrupted rendering features with geometry-aligned proxy queries and restricts cross-view attention to local windows, enabling robust diffusion-based refinement under extreme view sparsity.
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SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo
Procedural rules with NURBS generate MVS training data that outperforms same-scale manual curation and matches or exceeds larger manual datasets.