MAPR improves adversarial robustness in 3D point cloud networks by aligning latent predictions with intrinsic manifold geometry via curvature/diffusion features and a consistency loss.
Pointcat: Contrastive adversar- ial training for robust point cloud recognition
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cs.CV 2years
2026 2verdicts
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ReMATF proposes a lightweight recurrent multi-scale network for atmospheric turbulence mitigation in dynamic videos that uses two-frame recurrent processing with motion-adaptive per-pixel fusion to enhance efficiency and coherence.
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Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness
MAPR improves adversarial robustness in 3D point cloud networks by aligning latent predictions with intrinsic manifold geometry via curvature/diffusion features and a consistency loss.
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ReMATF: Recurrent Motion-Adaptive Multi-scale Turbulence Mitigation for Dynamic Scenes
ReMATF proposes a lightweight recurrent multi-scale network for atmospheric turbulence mitigation in dynamic videos that uses two-frame recurrent processing with motion-adaptive per-pixel fusion to enhance efficiency and coherence.