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.
Implicit geometric regularization for learning shapes
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Optimal INR freeze depth matches highest weight stable rank layer; SAEs reveal SIREN atoms are localized while FFMLP atoms trace cohort contours with causal impact on PSNR.
SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
NeuS introduces a bias-free volume rendering method for signed distance function representations to reconstruct accurate surfaces from 2D images.
EpiS improves generalizable neural surface reconstruction from sparse views by guiding epipolar feature aggregation with cost volumes, using an epipolar transformer, and applying pretrained monocular depth constraints, outperforming prior methods on DTU and BlendedMVS.
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.
FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.
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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What Cohort INRs Encode and Where to Freeze Them
Optimal INR freeze depth matches highest weight stable rank layer; SAEs reveal SIREN atoms are localized while FFMLP atoms trace cohort contours with causal impact on PSNR.
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SpUDD: Superpower Contouring of Unsigned Distance Data
SpUDD defines superpower contours from power diagrams of unsigned distance samples, proves convergence to the true surface, and uses them to generate approximating polygonal meshes that outperform prior strategies.
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NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
NeuS introduces a bias-free volume rendering method for signed distance function representations to reconstruct accurate surfaces from 2D images.
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Neural Surface Reconstruction from Sparse Views Using Epipolar Geometry
EpiS improves generalizable neural surface reconstruction from sparse views by guiding epipolar feature aggregation with cost volumes, using an epipolar transformer, and applying pretrained monocular depth constraints, outperforming prior methods on DTU and BlendedMVS.
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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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First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstruction
FSTM improves indoor reconstruction by training geometry first without semantic supervision, then adding semantics, achieving 2.3x faster training and higher object surface recall than joint optimization.