GuardMarkGS unifies watermarking and adversarial edit deterrence into a single optimization framework for protecting 3D Gaussian Splatting assets.
Generalized resampled importance sampling: Foundations of ReSTIR.ACM Trans- actions on Graphics (Proc
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
DP-GCL improves differentially private contrastive learning by bounding group-level contributions through batch partitioning and intra-group augmentation, delivering 5.6% higher image classification accuracy and 20.1% higher retrieval accuracy than existing approaches.
CARV amortizes upstream diffusion teacher costs over noise resamples with timestep importance sampling and stratified-inverse-CDF sampling, delivering 2-3x effective compute gains in text-to-3D experiments and order-of-magnitude variance cuts in single-step distillation.
citing papers explorer
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GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
GuardMarkGS unifies watermarking and adversarial edit deterrence into a single optimization framework for protecting 3D Gaussian Splatting assets.
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Differentially Private Contrastive Learning via Bounding Group-level Contribution
DP-GCL improves differentially private contrastive learning by bounding group-level contributions through batch partitioning and intra-group augmentation, delivering 5.6% higher image classification accuracy and 20.1% higher retrieval accuracy than existing approaches.
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Variance Reduction for Expectations with Diffusion Teachers
CARV amortizes upstream diffusion teacher costs over noise resamples with timestep importance sampling and stratified-inverse-CDF sampling, delivering 2-3x effective compute gains in text-to-3D experiments and order-of-magnitude variance cuts in single-step distillation.