Single-View Height Estimation with Conditional Diffusion Probabilistic Models
This addresses the problem of expensive multi-view data acquisition for height estimation in remote sensing, though it is incremental as it applies existing diffusion methods to a new domain.
The paper tackles single-view height estimation from remote sensing imagery by using conditional diffusion probabilistic models to generate high-resolution 3D surfaces, showing promising results on the Vaihingen benchmark dataset.
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.