CUBE encodes 3D faces via a grid of learned high-dimensional B-spline features that map parametrically to a base shape plus MLP-refined displacements, enabling dense correspondence and state-of-the-art registration from point clouds or images.
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5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Trained correlated-photon illumination paired with a Transformer backend improves object classification accuracy by up to 15 percentage points in photon-starved noisy imaging.
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
ShapeY is a benchmark dataset and nearest-neighbor protocol that measures shape-based recognition in vision models, revealing that even state-of-the-art networks fail to generalize consistently across 3D viewpoints and non-shape appearance changes.
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.
citing papers explorer
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Representing 3D Faces with Learnable B-Spline Volumes
CUBE encodes 3D faces via a grid of learned high-dimensional B-spline features that map parametrically to a base shape plus MLP-refined displacements, enabling dense correspondence and state-of-the-art registration from point clouds or images.
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Ultra-low-light computer vision using trained photon correlations
Trained correlated-photon illumination paired with a Transformer backend improves object classification accuracy by up to 15 percentage points in photon-starved noisy imaging.
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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching
ShapeY is a benchmark dataset and nearest-neighbor protocol that measures shape-based recognition in vision models, revealing that even state-of-the-art networks fail to generalize consistently across 3D viewpoints and non-shape appearance changes.
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One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning
Non-linear transformers enable cross-domain generalization in in-context RL by representing value functions from different domains with shared weights inside a shared RKHS.