SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
Hisplat: Hierarchical 3d gaussian splatting for generalizable sparse-view reconstruction
7 Pith papers cite this work. Polarity classification is still indexing.
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SatSurfGS improves sparse-view satellite surface reconstruction accuracy and generalization by adding confidence-aware monocular-multi-view fusion, cross-stage residual guidance, and bidirectional routing loss to 2D Gaussian Splatting.
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
A feed-forward model regresses accurate Gaussian surfel geometry from sparse views using Nyquist-guided cross-view feature aggregation, achieving 100x speedup over optimization-based 3DGS surface methods on DTU benchmarks.
UniRecGen unifies reconstruction and generation via shared canonical space and disentangled cooperative learning to produce complete, consistent 3D models from sparse views.
SwiftGS uses episodic meta-training to predict geometry-radiation-decoupled Gaussian primitives and a lightweight SDF for zero-shot 3D satellite surface reconstruction with physics-aware rendering.
RoSplat adds alpha normalization for brightness consistency across varying input views and a 3D sampling regularizer to mitigate hole artifacts in high-resolution feed-forward Gaussian splatting.
citing papers explorer
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SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis
SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
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SatSurfGS: Generalizable 2D Gaussian Splatting for Sparse-View Satellite Surface Reconstruction
SatSurfGS improves sparse-view satellite surface reconstruction accuracy and generalization by adding confidence-aware monocular-multi-view fusion, cross-stage residual guidance, and bidirectional routing loss to 2D Gaussian Splatting.
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Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective
The paper proposes a problem-driven taxonomy for feed-forward 3D scene modeling that groups methods by five core challenges: feature enhancement, geometry awareness, model efficiency, augmentation strategies, and temporal-aware modeling.
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SurfelSplat: Learning Efficient and Generalizable Gaussian Surfel Representations for Sparse-View Surface Reconstruction
A feed-forward model regresses accurate Gaussian surfel geometry from sparse views using Nyquist-guided cross-view feature aggregation, achieving 100x speedup over optimization-based 3DGS surface methods on DTU benchmarks.
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UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
UniRecGen unifies reconstruction and generation via shared canonical space and disentangled cooperative learning to produce complete, consistent 3D models from sparse views.
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SwiftGS: Episodic Priors for Immediate Satellite Surface Recovery
SwiftGS uses episodic meta-training to predict geometry-radiation-decoupled Gaussian primitives and a lightweight SDF for zero-shot 3D satellite surface reconstruction with physics-aware rendering.
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RoSplat: Robust Feed-Forward Pixel-wise Gaussian Splatting for Varying Input Views and High-Resolution Rendering
RoSplat adds alpha normalization for brightness consistency across varying input views and a 3D sampling regularizer to mitigate hole artifacts in high-resolution feed-forward Gaussian splatting.