增强多边形建筑分割：基于定向角点
Enhancing Polygonal Building Segmentation via Oriented Corners
AI总结：
本文提出OriCornerNet，一种直接提取建筑多边形的深度卷积网络，通过预测掩膜、角点和定向向量，结合图卷积网络迭代细化，在SpaceNet Vegas和CrowdAI-small数据集上达到先进水平。
摘要：
随着各种应用对高分辨率地图的需求日益增长，从俯视图像中准确分割建筑矢量已成为必要。然而，当前的深度神经网络通常产生栅格数据输出，导致需要大量的后处理，这损害了建筑表示的保真度、规则性和简洁性。为此，本文提出了一种名为OriCornerNet的新型深度卷积神经网络，它直接从输入图像中提取描绘好的建筑多边形。具体而言，我们的方法涉及一个深度模型，该模型预测建筑足迹掩膜、角点以及指示朝向相邻角点方向的定向向量。这些预测随后用于重建初始多边形，然后通过利用语义和几何特征的图卷积网络进行迭代细化。我们的方法通过用预测的角点初始化细化过程，固有地生成简化的多边形。此外，包含来自定向角点的几何信息有助于产生更规则和更准确的结果。在SpaceNet Vegas和CrowdAI-small数据集上进行的性能评估表明，与俯视图像建筑分割的最先进方法相比，我们的方法具有竞争力的有效性。
英文摘要：
The growing demand for high-resolution maps across various applications has underscored the necessity of accurately segmenting building vectors from overhead imagery. However, current deep neural networks often produce raster data outputs, leading to the need for extensive post-processing that compromises the fidelity, regularity, and simplicity of building representations. In response, this paper introduces a novel deep convolutional neural network named OriCornerNet, which directly extracts delineated building polygons from input images. Specifically, our approach involves a deep model that predicts building footprint masks, corners, and orientation vectors that indicate directions toward adjacent corners. These predictions are then used to reconstruct an initial polygon, followed by iterative refinement using a graph convolutional network that leverages semantic and geometric features. Our method inherently generates simplified polygons by initializing the refinement process with predicted corners. Also, including geometric information from oriented corners contributes to producing more regular and accurate results. Performance evaluations conducted on SpaceNet Vegas and CrowdAI-small datasets demonstrate the competitive efficacy of our approach compared to the state-of-the-art in building segmentation from overhead imagery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
