COVScene is a pose-free framework that lifts semantic Gaussians into a volumetric occupancy field during training to jointly support novel view synthesis, open-vocabulary segmentation, and semantic occupancy prediction.
Spatialsplat: Efficient semantic 3d from sparse unposed images
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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.
FLEG reconstructs language-embedded 3D Gaussians from arbitrary input views using a dual-branch distillation framework and a sparse set of semantic Gaussians that requires only 5% of prior embeddings.
C3G creates compact 3D Gaussian representations with 2K points by guiding placement via learnable tokens that aggregate multi-view features through attention, yielding better efficiency and performance than dense methods.
UniSplat learns consistent 3D geometry, appearance, and semantics from unposed images using dual masking, progressive Gaussian splatting, and recalibration to align predictions across tasks.
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
citing papers explorer
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Bridging 3D Gaussians and Semantic Occupancy for Comprehensive Open-Vocabulary Scene Understanding from Unposed Images
COVScene is a pose-free framework that lifts semantic Gaussians into a volumetric occupancy field during training to jointly support novel view synthesis, open-vocabulary segmentation, and semantic occupancy prediction.
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GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
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.
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FLEG: Feed-Forward Language Embedded Gaussian Splatting from Any Views via Compact Semantic Representation
FLEG reconstructs language-embedded 3D Gaussians from arbitrary input views using a dual-branch distillation framework and a sparse set of semantic Gaussians that requires only 5% of prior embeddings.
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C3G: Learning Compact 3D Representations with 2K Gaussians
C3G creates compact 3D Gaussian representations with 2K points by guiding placement via learnable tokens that aggregate multi-view features through attention, yielding better efficiency and performance than dense methods.
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Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images
UniSplat learns consistent 3D geometry, appearance, and semantics from unposed images using dual masking, progressive Gaussian splatting, and recalibration to align predictions across tasks.
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A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.