GazePrior learns a 3D prior over eyes to synthesize realistic ground-truth data for training eye trackers on new devices without new real data collection.
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Nerf: Representing scenes as neural radiance fields for view synthesis.Communications of the ACM, 65(1):99–106
10 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 10representative citing papers
Spectral Energy Centroid is a new metric that quantifies signal frequency and INR spectral bias, supporting better hyperparameter selection and cross-architecture analysis.
H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
PhySPRING uses differentiable GNNs to learn hierarchical coarsened spring-mass topologies and parameters from observations, delivering up to 2.3x speedup on PhysTwin benchmarks and comparable robot policy success rates in zero-shot Real2Sim substitution.
Geo-EVS improves extrapolative novel view synthesis for driving scenes by conditioning on geometric maps from reprojections and training with artifact masks, leading to better quality and 3D detection on Waymo.
A new sparse-view 3D Gaussian splatting method for unconstrained scenes with distractors combines diffusion-based reference-guided refinement and sparsity-aware Gaussian replication to achieve better rendering quality.
PEPS decomposes positional encodings into projected points with unique frequency-dependent motions to support more efficient learned grid-based encodings in INRs, outperforming prior methods on image, texture, and SDF tasks with often 25% fewer parameters.
A reference-free bootstrapped cross-validation method estimates performance of 4D deep-learning reconstruction from sparse X-ray data by comparing outputs from independent data subsets.
A quota-governor for Gaussian Splatting that tracks a quadratic target point count by adjusting existing hyperparameters, reaching the target by 15k iterations without hard cutoffs for fairer evaluations.
Proposes Lipschitz regularization during fine-tuning to prevent distributional drift in personalized diffusion models, improving subject fidelity and prompt adherence.
citing papers explorer
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GazePrior: Zero-Shot AR/VR Eye Tracking via Learned 3D Gaze Reconstruction
GazePrior learns a 3D prior over eyes to synthesize realistic ground-truth data for training eye trackers on new devices without new real data collection.
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Spectral Energy Centroid: a Metric for Improving Performance and Analyzing Spectral Bias in Implicit Neural Representations
Spectral Energy Centroid is a new metric that quantifies signal frequency and INR spectral bias, supporting better hyperparameter selection and cross-architecture analysis.
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H2G: Hierarchy-Aware Hyperbolic Grouping for 3D Scenes
H2G distills 2D foundation-model affinities into a Lorentz hyperbolic feature field that represents hierarchical 3D groupings at multiple granularities.
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PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN
PhySPRING uses differentiable GNNs to learn hierarchical coarsened spring-mass topologies and parameters from observations, delivering up to 2.3x speedup on PhysTwin benchmarks and comparable robot policy success rates in zero-shot Real2Sim substitution.
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Geo-EVS: Geometry-Conditioned Extrapolative View Synthesis for Autonomous Driving
Geo-EVS improves extrapolative novel view synthesis for driving scenes by conditioning on geometric maps from reprojections and training with artifact masks, leading to better quality and 3D detection on Waymo.
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Sparse-View 3D Gaussian Splatting in the Wild
A new sparse-view 3D Gaussian splatting method for unconstrained scenes with distractors combines diffusion-based reference-guided refinement and sparsity-aware Gaussian replication to achieve better rendering quality.
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PEPS: Positional Encoding Projected Sampling -- Extended
PEPS decomposes positional encodings into projected points with unique frequency-dependent motions to support more efficient learned grid-based encodings in INRs, outperforming prior methods on image, texture, and SDF tasks with often 25% fewer parameters.
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An evaluation framework for sparse 4D (3D + time) imaging reconstruction via bootstrapped cross-validation
A reference-free bootstrapped cross-validation method estimates performance of 4D deep-learning reconstruction from sparse X-ray data by comparing outputs from independent data subsets.
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Smart target point control for Gaussian Splatting methods
A quota-governor for Gaussian Splatting that tracks a quadratic target point count by adjusting existing hyperparameters, reaching the target by 15k iterations without hard cutoffs for fairer evaluations.
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Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift
Proposes Lipschitz regularization during fine-tuning to prevent distributional drift in personalized diffusion models, improving subject fidelity and prompt adherence.