ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation

• URL: http://arxiv.org/abs/2605.22556v1
• Date: Thu, 21 May 2026 14:39:24 GMT
• Title: ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation
• Abstract summary: ImplicitTerrainV2 combines a spectral control mechanism with wavelet-guided spatial adaptivity, derivative-aware supervision, and post-training model compression.<n>On 50 Swiss terrain tiles, ImplicitTerrainV2 reaches 66.25 dB end-to-end PSNR, improving over the prior work by 5.70 dB while using 3.2x fewer parameters and training in 55 s per tile on a single GPU.
• Score: 10.658180726482119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital elevation models (DEMs) underpin terrain analysis in Geographic Information Systems (GIS), but in their common raster form, they rely on interpolation for off-grid sampling and finite-difference operators for derivative-based analysis. Implicit neural representations (INRs) offer a continuous alternative, but prior terrain INRs lack explicit frequency control, neglect the gradient structure of terrain, and remain too large and costly to train for practical deployment. We present ImplicitTerrainV2, which advances terrain INRs toward a compact, efficient neural terrain data format by combining a spectral control mechanism with wavelet-guided spatial adaptivity, derivative-aware supervision, and post-training model compression. At its core, a wavelet complexity field (WCF) derives spatially-adaptive frequency masks from analytically computed wavelet coefficients, localizing high-frequency capacity to complex terrain regions. The same field guides complexity-aware adaptive sampling that concentrates training in high-complexity regions, while gradient matching applies extra supervision to enforce the smooth manifold structure of terrain DEMs for improved derivative fidelity. Post-training mixed-precision quantization and entropy coding reduce storage to 1.23 bpp with a 0.28 dB PSNR drop. On 50 Swiss terrain tiles, ImplicitTerrainV2 reaches 66.25 dB end-to-end PSNR, improving over the prior work by 5.70 dB while using 3.2x fewer parameters and training in 55 s per tile on a single GPU. Our compressed neural format is competitive with several established DEM codecs in rate-distortion performance, while additionally supporting off-grid point queries, closed-form derivative evaluation, and resolution-independent reconstruction, which may benefit many downstream GIS applications.

Related papers

• CRFCAN: A Complex-Valued Cross-Domain Residual Network for Joint Channel and Phase Noise Estimation in Sub-THz OFDM Systems [5.464031544932083]
  In sub-terahertz (sub-THz) communications, the coupling of ultra-wide bandwidth and severe phase noise (PN) impairments renders joint channel and PN estimation highly complex and computationally prohibitive.<n>We propose CRFCAN, a complex-valued residual FFT convolutional attention network designed for joint channel and PN estimation.
  arXiv  Detail & Related papers  (2026-09-10T22:02:08Z)
• Machine Learning-Based Reconstruction for Resistive Silicon Sensors [53.365453214937496]
  We study machine-learning-based reconstruction and compression for resistive silicon sensors.<n>We use full-waveform information from correlated pads to regularise the reconstruction and extract spatial information.<n>We first introduce recurrent neural network models based on LSTM layers, which provide a proof-of-concept implementation for full-waveform reconstruction.
  arXiv  Detail & Related papers  (2026-07-13T14:03:26Z)
• Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data [10.658180726482119]
  Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function.<n>For terrain elevation data, this supports analytic derivatives, arbitrary-resolution decoding, and a smooth surface model of the underlying heightfield.<n>Amortized neural representations reduce this cost with a shared network.<n>A new tile is mapped to a compact per-tile payload, and a shared decoder reconstructs the heightfield from it.
  arXiv  Detail & Related papers  (2026-05-29T22:44:52Z)
• SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces [91.71563221166417]
  Implicit neural representations are powerful for geometric modeling, but their practical use is often limited by the high computational cost.<n>We propose an efficient neural implicit geometry representation framework with spatially adaptive network depth.
  arXiv  Detail & Related papers  (2026-04-15T14:14:17Z)
• Compressive sensing inspired self-supervised single-pixel imaging [48.79613794195208]
  SISTA-Net is a compressive sensing-inspired self-supervised method for single-pixel imaging.<n>We leverage deep nonlinear networks as adaptive sparse transforms combined with a learnable soft-thresholding operator to impose explicit physical sparsity in the latent domain.<n>Experiments on multiple simulation scenarios demonstrate that SISTA-Net outperforms state-of-the-art methods by 2.6 dB in PSNR.
  arXiv  Detail & Related papers  (2026-03-31T13:31:43Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• Adaptive Mesh-Quantization for Neural PDE Solvers [51.26961483962011]
  Graph Neural Networks can handle the irregular meshes required for complex geometries and boundary conditions, but still apply uniform computational effort across all nodes.<n>We propose Adaptive Mesh Quantization: spatially adaptive quantization across mesh node, edge, and cluster features, dynamically adjusting the bit-width used by a quantized model.<n>We demonstrate our framework's effectiveness by integrating it with two state-of-the-art models, MP-PDE and GraphViT, to evaluate performance across multiple tasks.
  arXiv  Detail & Related papers  (2025-11-23T14:47:24Z)
• ReVeal-MT: A Physics-Informed Neural Network for Multi-Transmitter Radio Environment Mapping [9.43653276377036]
  We propose emphReVeal-MT, a novel PINN which integrates the multi-source PDE residual into a neural network loss function.<n>ReVeal-MT is validated using real-world measurements from the ARA wireless living lab across rural and suburban environments.<n>Results show that ReVeal-MT achieves substantial accuracy gains in multi-transmitter scenarios.
  arXiv  Detail & Related papers  (2025-11-22T23:33:06Z)
• FieldFormer: Physics-Informed Transformers for Spatio-Temporal Field Reconstruction from Sparse Sensors [4.027064713296126]
  We introduce FieldFormer, a transformer-based framework for mesh-free-temporal field reconstruction.<n>For each query, FieldFormer gathers a local neighborhood using a learnable velocity-scaled distance metric.<n>Our results demonstrate that FieldFormer enables accurate (RMSE$10-2$), efficient, and physically consistent field reconstruction from sparse (0.4%-2%) and noisy(10%) data.
  arXiv  Detail & Related papers  (2025-10-04T00:37:28Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation [22.40154714677385]
  We introduce the Adaptive Fortified Transformer (AdaFortiTran) to enhance channel estimation in challenging environments.<n>AdaFortiTran achieves up to 6 dB reduction in mean squared error (MSE) compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2025-05-14T02:22:37Z)
• DETR Doesn't Need Multi-Scale or Locality Design [69.56292005230185]
  This paper presents an improved DETR detector that maintains a "plain" nature. It uses a single-scale feature map and global cross-attention calculations without specific locality constraints. We show that two simple technologies are surprisingly effective within a plain design to compensate for the lack of multi-scale feature maps and locality constraints.
  arXiv  Detail & Related papers  (2023-08-03T17:59:04Z)
• Learning Regionalization using Accurate Spatial Cost Gradients within a Differentiable High-Resolution Hydrological Model: Application to the French Mediterranean Region [0.18139022013189662]
  Estimating distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem. This paper introduces a Hybrid Assimilation and Regionalization (HDA-PR) approach incorporating learnable regionalization mappings. Results highlight a strong regionalization of HDA-PR especially in the most challenging upstream-to-downstream extrapolation scenario.
  arXiv  Detail & Related papers  (2023-08-02T07:23:50Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.