Poles and building edges are frequently observable objects on urban roads,
conveying reliable hints for various computer vision tasks. To repetitively
extract them as features and perform association between discrete LiDAR frames
for registration, we propose the first learning-based feature segmentation and
description model for 3D lines in LiDAR point cloud. To train our model without
the time consuming and tedious data labeling process, we first generate
synthetic primitives for the basic appearance of target lines, and build an
iterative line auto-labeling process to gradually refine line labels on real
LiDAR scans. Our segmentation model can extract lines under arbitrary scale
perturbations, and we use shared EdgeConv encoder layers to train the two
segmentation and descriptor heads jointly. Base on the model, we can build a
highly-available global registration module for point cloud registration, in
conditions without initial transformation hints. Experiments have demonstrated
that our line-based registration method is highly competitive to
state-of-the-art point-based approaches. Our code is available at
this https URL

基于线特征的 LiDAR 点云点云配准方法通过 EdgeConv 标记线特征，并使用共享编码器层来训练两个分割和描述头，该方法在无初始变换输入的情况下验证了自己的有效性，对于各种计算机视觉任务具有广泛的应用价值。