In this paper, we present an end-to-end 3D building wireframe reconstruction
method to regress edges directly from aerial LiDAR point clouds.Our method,
named Parametric Building Wireframe Reconstruction (PBWR), takes aerial LiDAR
point clouds and initial edge entities as input, and fully uses self-attention
mechanism of transformers to regress edge parameters without any intermediate
steps such as corner prediction. We propose an edge non-maximum suppression
(E-NMS) module based on edge similarityto remove redundant edges. Additionally,
a dedicated edge loss function is utilized to guide the PBWR in regressing
edges parameters, where simple use of edge distance loss isn't suitable. In our
experiments, we demonstrate state-of-the-art results on the Building3D dataset,
achieving an improvement of approximately 36% in entry-level dataset edge
accuracy and around 42% improvement in the Tallinn dataset.

本文提出了一种从航空 LiDAR 点云直接回归边缘的端到端三维建筑线框重建方法，通过使用 transformer 的自注意机制，以航空 LiDAR 点云和初始边界实体作为输入，无需角点预测等中间步骤来回归边缘参数；我们提出了一种基于边界相似性的边缘非极大抑制 (E-NMS) 模块来移除冗余边缘；此外，我们使用了专用的边缘损失函数来引导 PBWR 回归边缘参数，简单的使用边缘距离损失并不适合；在实验中，我们在 Building3D 数据集上取得了最先进的结果，边缘精度提高约 36%，在 Tallinn 数据集上提高了约 42%。