FLUIDSPLAT models flow fields with K anisotropic Gaussian primitives, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives optimal K scaling with N sensors, and reports 11-28% error reduction on four flow benchmarks.
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=
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AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
A relightable Gaussian Splatting method for virtual production decomposes scenes into fixed appearance and variable lighting by parameterizing primitives to directly sample high-resolution background textures, enabling controllable relighting without physically-based rendering or far-field maps.
DeG models 3D Gaussians via learned octree density and uses VecSeq Sobol re-indexing to turn set generation into sequence modeling, claiming SOTA quality in single-image-to-3D.
ReorgGS reorganizes the Gaussian distribution in converged 3DGS models by resampling centers and covariances to reduce parameterization degeneration and enable better subsequent optimization.
GADA corrects spatial misalignments in warped images for Gaussian Splatting via iterative deformable offsets and confidence-weighted fusion, yielding higher quality and 2.13x faster FPS than prior warping methods.
citing papers explorer
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FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives
FLUIDSPLAT models flow fields with K anisotropic Gaussian primitives, proves an O(K^{-s/d}) approximation rate under Sobolev smoothness s, derives optimal K scaling with N sensors, and reports 11-28% error reduction on four flow benchmarks.
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Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
AmbiSuR adds intrinsic photometric disambiguation and a self-indication module to Gaussian Splatting to resolve ambiguities and improve surface reconstruction accuracy.
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Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination
A relightable Gaussian Splatting method for virtual production decomposes scenes into fixed appearance and variable lighting by parameterizing primitives to directly sample high-resolution background textures, enabling controllable relighting without physically-based rendering or far-field maps.
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Generative 3D Gaussians with Learned Density Control
DeG models 3D Gaussians via learned octree density and uses VecSeq Sobol re-indexing to turn set generation into sequence modeling, claiming SOTA quality in single-image-to-3D.
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ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting
ReorgGS reorganizes the Gaussian distribution in converged 3DGS models by resampling centers and covariances to reduce parameterization degeneration and enable better subsequent optimization.
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GADA: Geometry-Aware Deformable Aggregation for Image-Based Gaussian Splatting
GADA corrects spatial misalignments in warped images for Gaussian Splatting via iterative deformable offsets and confidence-weighted fusion, yielding higher quality and 2.13x faster FPS than prior warping methods.
- Signal Structure-Aware Gaussian Splatting for Large-Scale Scene Reconstruction