LiDAR odometry estimation and 3D semantic segmentation are crucial for
autonomous driving, which has achieved remarkable advances recently. However,
these tasks are challenging due to the imbalance of points in different
semantic categories for 3D semantic segmentation and the influence of dynamic
objects for LiDAR odometry estimation, which increases the importance of using
representative/salient landmarks as reference points for robust feature
learning. To address these challenges, we propose a saliency-guided approach
that leverages attention information to improve the performance of LiDAR
odometry estimation and semantic segmentation models. Unlike in the image
domain, only a few studies have addressed point cloud saliency information due
to the lack of annotated training data. To alleviate this, we first present a
universal framework to transfer saliency distribution knowledge from color
images to point clouds, and use this to construct a pseudo-saliency dataset
(i.e. FordSaliency) for point clouds. Then, we adopt point cloud-based
backbones to learn saliency distribution from pseudo-saliency labels, which is
followed by our proposed SalLiDAR module. SalLiDAR is a saliency-guided 3D
semantic segmentation model that integrates saliency information to improve
segmentation performance. Finally, we introduce SalLONet, a self-supervised
saliency-guided LiDAR odometry network that uses the semantic and saliency
predictions of SalLiDAR to achieve better odometry estimation. Our extensive
experiments on benchmark datasets demonstrate that the proposed SalLiDAR and
SalLONet models achieve state-of-the-art performance against existing methods,
highlighting the effectiveness of image-to-LiDAR saliency knowledge transfer.
Source code will be available at this https URL

提出一种基于显著性引导的方法，通过利用注意力信息来改善激光雷达里程计估计和语义分割模型的性能，并通过从彩色图像到点云的显著性知识转移，构建了伪显著性数据集，提出了一种显著性引导的三维语义分割模型和基于自我监督的显著性引导激光雷达里程计网络，并在基准数据集上进行了广泛的实验，取得了最先进的性能。