mlsanityData to 14 Oct 2026

Single-View Height Estimation with Conditional Diffusion Probabilistic Models

arXiv 2304.13214 ↗First posted 26 Apr 2023cs.CV cs.LG eess.IVcited by 1

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Digital Surface Models (DSM) offer a wealth of height information for understanding the Earth's surface as well as monitoring the existence or change in natural and man-made structures. Classical height estimation requires multi-view geospatial imagery or LiDAR point clouds which can be expensive to acquire. Single-view height estimation using neural network based models shows promise however it can struggle with reconstructing high resolution features. The latest advancements in diffusion models for high resolution image synthesis and editing have yet to be utilized for remote sensing imagery, particularly height estimation. Our approach involves training a generative diffusion model to learn the joint distribution of optical and DSM images across both domains as a Markov chain. This is accomplished by minimizing a denoising score matching objective while being conditioned on the source image to generate realistic high resolution 3D surfaces. In this paper we experiment with conditional denoising diffusion probabilistic models (DDPM) for height estimation from a single remotely sensed image and show promising results on the Vaihingen benchmark dataset.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 14 of its 30 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

14 of 30 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  6. 2020Denoising Diffusion Implicit Modelscited by 9,781
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  8. 2022Generative Adversarial Networkscited by 5,627
  9. 2015Variational Inference with Normalizing Flowscited by 2,170
  10. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  11. 2021Remote Sensing Image Change Detection with Transformerscited by 306
  12. 2018IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Networkcited by 31
2 more
  1. 2022Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusioncited by 8
  2. 2022Supervising Remote Sensing Change Detection Models with 3D Surface Semanticscited by 4

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectivescited by 27

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  1. 2024Fast LiDAR Upsampling using Conditional Diffusion Models
  2. 2026Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators
  3. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models
  4. 2023Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs
  5. 2023EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
  6. 2026GeoHeight-Bench: Towards Height-Aware Multimodal Reasoning in Remote Sensing
  7. 2025A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction
  8. 2026Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning