RPC-GS enables native RPC-based rendering in Gaussian Splatting for satellite imagery by chaining geo-coordinate transformations and a Jacobian covariance projection, yielding lower reconstruction errors than perspective or affine approximations on DFC2019 and IARPA2016 benchmarks.
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Wildgaussians: 3d gaussian splatting in the wild
13 Pith papers cite this work. Polarity classification is still indexing.
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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.
ADM-GS decomposes static background appearance into traversal-invariant material and traversal-dependent illumination via a frequency-separated neural light field, yielding +0.98 dB PSNR gains and better cross-traversal consistency on Argoverse 2 and Waymo data.
ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.
RefineSplat applies entropy-aware adaptive masking and density control to 3DGS to remove color- or semantically ambiguous distractors, validated on a new 18-scene Ambiguous wild dataset with claimed SOTA results.
EPS3D is an end-to-end architecture for 3D panoptic segmentation from multi-view images that uses distillation and semantic-instance mutual enhancement to achieve higher benchmark performance and speed than prior methods.
3D Skew Gaussian Splatting extends standard 3D Gaussian Splatting with skew primitives, enhanced opacity, depth-aware densification, and a re-derived CUDA pipeline for a free-camera visualization engine.
A new sparse-view 3D Gaussian splatting method for unconstrained scenes with distractors combines diffusion-based reference-guided refinement and sparsity-aware Gaussian replication to achieve better rendering quality.
DualSplat bootstraps object-level pseudo-masks from initial 3DGS reconstruction failures using residuals and SAM2 to enable robust second-pass optimization in transient-heavy scenes.
GA-GS uses motion segmentation, diffusion-based inpainting for pseudo-ground-truth, and per-Gaussian authenticity scalars to achieve SOTA static scene reconstruction from videos with dynamic occlusions.
FACT-GS allocates higher texture sampling density to high-frequency areas in 2D Gaussian Splatting through a learnable deformation field, recovering sharper details at the same parameter budget.
LCD-GS decouples luminance and chromaticity in 3D Gaussian Splatting to handle extreme radiance variations better than prior multi-exposure methods while using a simpler architecture.
HarmoGS adds semantic consistency-guided masking and dual-view orthogonal gradient harmonization to 3D Gaussian Splatting to reduce artifacts from distractors and cross-view illumination inconsistencies.
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FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
FACT-GS allocates higher texture sampling density to high-frequency areas in 2D Gaussian Splatting through a learnable deformation field, recovering sharper details at the same parameter budget.
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High Dynamic Range 3D Gaussian Splatting via Luminance-Chromaticity Decomposition
LCD-GS decouples luminance and chromaticity in 3D Gaussian Splatting to handle extreme radiance variations better than prior multi-exposure methods while using a simpler architecture.