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Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion

2022-12-01 18:59:26
Benedikt Kolbeinsson, Krystian Mikolajczyk
arXiv_CV
               
Abstract

Semantic segmentation from aerial views is a vital task for autonomous drones as they require precise and accurate segmentation to traverse safely and efficiently. Segmenting images from aerial views is especially challenging as they include diverse view-points, extreme scale variation and high scene complexity. To address this problem, we propose an end-to-end multi-class semantic segmentation diffusion model. We introduce recursive denoising which allows predicted error to propagate through the denoising process. In addition, we combine this with a hierarchical multi-scale approach, complementary to the diffusion process. Our method achieves state-of-the-art results on UAVid and on the Vaihingen building segmentation benchmark.

Abstract (translated)

URL

https://arxiv.org/abs/2212.00787

PDF

https://arxiv.org/pdf/2212.00787.pdf

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