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.
Neural elevation models for terrain mapping and path planning
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A novel explicit neural height field method for descent-phase wide-angle imagery achieves greater spatial coverage than multi-view stereo while preserving estimation accuracy on simulated planetary terrains.
Introduces a terrain-specific benchmark showing cross-domain gaps in INR methods and demonstrates that HUVR+SIREN achieves superior height and derivative fidelity in a compact quantized format.
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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Neural 3D Reconstruction of Planetary Surfaces from Descent-Phase Wide-Angle Imagery
A novel explicit neural height field method for descent-phase wide-angle imagery achieves greater spatial coverage than multi-view stereo while preserving estimation accuracy on simulated planetary terrains.
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Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data
Introduces a terrain-specific benchmark showing cross-domain gaps in INR methods and demonstrates that HUVR+SIREN achieves superior height and derivative fidelity in a compact quantized format.