论文标题
RSKDD-NET：基于随机样本的键盘检测器和描述符
RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor
论文作者
论文摘要
关键点检测器和描述符是点云注册的两个主要组成部分。以前基于学习的关键点检测器依赖于每个点或最远的点样本（FPS）的显着性估计，用于候选点的选择，这些选择效率低下且不适用于大型场景。本文提出了用于大规模点云注册的随机基于样本的关键点检测器和描述符网络（RSKDD-NET）。关键思想是使用随机抽样有效地选择候选点，并使用基于学习的方法共同生成关键点和描述符。为了解决随机抽样的信息丢失，我们利用一种新型的随机扩张群集策略来扩大每个采样点的接受场和注意机制，以汇总邻居点的位置和特征。此外，我们提出匹配损失，以弱监督的方式训练描述符。在两个大型室外激光雷达数据集上进行的大量实验表明，拟议的RSKDD-NET实现了最先进的性能，其性能比现有方法快15倍以上。我们的代码可在https://github.com/ispc-lab/rskdd-net上找到。
Keypoint detector and descriptor are two main components of point cloud registration. Previous learning-based keypoint detectors rely on saliency estimation for each point or farthest point sample (FPS) for candidate points selection, which are inefficient and not applicable in large scale scenes. This paper proposes Random Sample-based Keypoint Detector and Descriptor Network (RSKDD-Net) for large scale point cloud registration. The key idea is using random sampling to efficiently select candidate points and using a learning-based method to jointly generate keypoints and descriptors. To tackle the information loss of random sampling, we exploit a novel random dilation cluster strategy to enlarge the receptive field of each sampled point and an attention mechanism to aggregate the positions and features of neighbor points. Furthermore, we propose a matching loss to train the descriptor in a weakly supervised manner. Extensive experiments on two large scale outdoor LiDAR datasets show that the proposed RSKDD-Net achieves state-of-the-art performance with more than 15 times faster than existing methods. Our code is available at https://github.com/ispc-lab/RSKDD-Net.
