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Found 6 papers, 2 papers with code
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
As a newly emerging advance in deep generative models, diffusion models have achieved state-of-the-art results in many fields, including computer vision, natural language processing, and molecule design.
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches.
SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models
The framework consists of a spectral-spatial diffusion module, and an attention-based classification module.
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models
In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process.
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.
Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.
