A wavelet-guided adaptive INR for DEMs achieves 66.25 dB PSNR on Swiss tiles with 3.2x fewer parameters than prior work, plus post-training compression to 1.23 bpp.
arXiv preprint arXiv:2105.02788 , year=
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SAD is a new explicit differentiable image representation based on soft anisotropic additively weighted Voronoi partitions that achieves higher PSNR and 4-19x faster training than Image-GS and Instant-NGP at matched bitrate.
AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
PREF introduces a phasor volume and tailored Fourier mapping to let shallow MLPs capture high-frequency signals compactly in 2D images, 3D SDFs, and 5D NeRFs.
IVGT implicitly models continuous neural scene representations from pose-free multi-view images to enable coherent surface extraction, novel view synthesis, and related 3D tasks via SDF and color prediction.
INRs parameterize signals as neural networks to enable continuous representations, analytical differentiation, and adaptive approximation spaces that address spectral bias through specialized activations and structured encodings.
citing papers explorer
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ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation
A wavelet-guided adaptive INR for DEMs achieves 66.25 dB PSNR on Swiss tiles with 3.2x fewer parameters than prior work, plus post-training compression to 1.23 bpp.
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Soft Anisotropic Diagrams for Differentiable Image Representation
SAD is a new explicit differentiable image representation based on soft anisotropic additively weighted Voronoi partitions that achieves higher PSNR and 4-19x faster training than Image-GS and Instant-NGP at matched bitrate.
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AIR: Amortized Image Reconstruction Framework for Self-Supervised Feed-Forward 2D Gaussian Splatting
AIR amortizes 2D Gaussian splatting into a self-supervised feed-forward network via residual stages, explicit stage control, and Predict-Optimize-Distill training.
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Neural Fields for NV-Center Inverse Sensing
NeTMY neural fields with annealed encoding, multiscale optimization, and spectrum-fidelity losses achieve superior localization and distributional accuracy in NV-center inverse sensing by using a tensor power-summed dipolar operator that exposes and mitigates center-collapse failures.
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PREF: Phasorial Embedding Fields for Compact Neural Representations
PREF introduces a phasor volume and tailored Fourier mapping to let shallow MLPs capture high-frequency signals compactly in 2D images, 3D SDFs, and 5D NeRFs.
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IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation
IVGT implicitly models continuous neural scene representations from pose-free multi-view images to enable coherent surface extraction, novel view synthesis, and related 3D tasks via SDF and color prediction.
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Implicit Neural Representations: A Signal Processing Perspective
INRs parameterize signals as neural networks to enable continuous representations, analytical differentiation, and adaptive approximation spaces that address spectral bias through specialized activations and structured encodings.