RoIPoly：使用顶点和对数嵌入的矢量化建筑轮廓提取
RoIPoly: Vectorized Building Outline Extraction Using Vertex and Logit Embeddings
AI总结：
提出RoIPoly，一种基于RoI查询的矢量化建筑轮廓提取方法，通过顶点查询和对数嵌入提高效率并去除冗余，在CrowdAI和Structured3D上表现优异。
摘要：
多边形建筑轮廓对于地理和制图应用至关重要。现有的从航空或卫星图像中提取轮廓的方法通常被分解为子任务，例如建筑掩膜和矢量化，或者将此任务视为有序顶点的序列到序列预测。前者缺乏效率，而后者常常生成冗余顶点，两者都导致次优性能。为了解决这些问题，我们提出了一种新颖的基于感兴趣区域（RoI）查询的方法，称为RoIPoly。具体来说，我们将每个顶点制定为一个查询，并将查询注意力限制在潜在建筑最相关的区域上，从而减少计算开销并实现更高效的顶点级交互。此外，我们引入了一种新颖的可学习对数嵌入，以促进注意力图上的顶点分类；因此，无需后处理即可去除冗余顶点。我们在矢量化建筑轮廓提取数据集CrowdAI和2D平面图重建数据集Structured3D上评估了我们的方法。在CrowdAI数据集上，使用ResNet50主干的RoIPoly在大多数MS-COCO指标上优于具有相同或更好主干的现有方法，尤其是在小型建筑上，并且在没有后处理的情况下在多边形质量和顶点冗余方面取得了有竞争力的结果。在Structured3D数据集上，我们的方法在大多数指标上取得了现有2D平面图重建方法中的第二好性能，展示了我们的跨域泛化能力。代码将在论文被接受后发布。
英文摘要：
Polygonal building outlines are crucial for geographic and cartographic applications. The existing approaches for outline extraction from aerial or satellite imagery are typically decomposed into subtasks, e.g., building masking and vectorization, or treat this task as a sequence-to-sequence prediction of ordered vertices. The former lacks efficiency, and the latter often generates redundant vertices, both resulting in suboptimal performance. To handle these issues, we propose a novel Region-of-Interest (RoI) query-based approach called RoIPoly. Specifically, we formulate each vertex as a query and constrain the query attention on the most relevant regions of a potential building, yielding reduced computational overhead and more efficient vertex level interaction. Moreover, we introduce a novel learnable logit embedding to facilitate vertex classification on the attention map; thus, no post-processing is needed for redundant vertex removal. We evaluated our method on the vectorized building outline extraction dataset CrowdAI and the 2D floorplan reconstruction dataset Structured3D. On the CrowdAI dataset, RoIPoly with a ResNet50 backbone outperforms existing methods with the same or better backbones on most MS-COCO metrics, especially on small buildings, and achieves competitive results in polygon quality and vertex redundancy without any post-processing. On the Structured3D dataset, our method achieves the second-best performance on most metrics among existing methods dedicated to 2D floorplan reconstruction, demonstrating our cross-domain generalization capability. The code will be released upon acceptance of this paper.
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。
