A variance-aware conditional MLP operating on 3D Gaussians corrects semantic errors from multi-view inconsistent 2D features to produce more accurate and robust 3D semantic Gaussian Splatting.
Feature 3dgs: Supercharging 3d gaussian splatting to enable distilled feature fields
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A hybrid structural latent points representation is learned by inserting a point-wise latent VAE into a point-cloud autoencoder and regularizing toward a Gaussian prior, paired with a lightweight 3DGS rendering pipeline, yielding gains on RLBench and ManiSkill2 benchmarks.
citing papers explorer
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NRGS: Neural Regularization for Robust 3D Semantic Gaussian Splatting
A variance-aware conditional MLP operating on 3D Gaussians corrects semantic errors from multi-view inconsistent 2D features to produce more accurate and robust 3D semantic Gaussian Splatting.
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Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation
A hybrid structural latent points representation is learned by inserting a point-wise latent VAE into a point-cloud autoencoder and regularizing toward a Gaussian prior, paired with a lightweight 3DGS rendering pipeline, yielding gains on RLBench and ManiSkill2 benchmarks.