论文标题
使用递归噪声扩散从鸟瞰图中的多级分割
Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion
论文作者
论文摘要
从空事视图中的语义细分是自动无人机的关键任务，因为它们依靠精确而准确的分割来安全有效地导航。但是，航空图像带来了独特的挑战，例如各种观点，极端规模的变化和较高的场景复杂性。在本文中，我们提出了一个解决这些挑战的端到端多级语义分割扩散模型。我们介绍了递归的denoisising，以允许信息通过denoising过程传播，以及一种补充扩散过程的层次多尺度方法。我们的方法在Vaihingen Building分段基准上的无人机数据集和最先进的性能上取得了令人鼓舞的结果。作为这种方法的第一次迭代，它对未来的改进显示了巨大的希望。
Semantic segmentation from aerial views is a crucial task for autonomous drones, as they rely on precise and accurate segmentation to navigate safely and efficiently. However, aerial images present unique challenges such as diverse viewpoints, extreme scale variations, and high scene complexity. In this paper, we propose an end-to-end multi-class semantic segmentation diffusion model that addresses these challenges. We introduce recursive denoising to allow information to propagate through the denoising process, as well as a hierarchical multi-scale approach that complements the diffusion process. Our method achieves promising results on the UAVid dataset and state-of-the-art performance on the Vaihingen Building segmentation benchmark. Being the first iteration of this method, it shows great promise for future improvements.
