4 accepted papers
Understanding the motion states of the surrounding environment is critical for safe autonomous driving. These motion states can be accurately derived from scene flow, which captures the three-dimensional motion field of points. Existing LiDAR scene flow methods extract spatial features from each poi
3D LiDAR-based perception has made remarkable advancements, leading to the widespread adoption of LiDAR in autonomous driving systems. Despite these technological strides, variations in LiDAR sensors and environmental conditions can significantly deteriorate the performance of perception models, pri…
"In the realm of LiDAR-based perception, significant strides have been made, yet domain generalization remains a substantial challenge. The performance often deteriorates when models are applied to unfamiliar datasets with different LiDAR sensors or deployed in new environments, primarily due to var…
Detecting traffic participants is an essential and age-old problem in autonomous driving. Recently, the recognition of moving objects has emerged as a major issue in this field for safe driving. In this paper, we present RVMOS, a LiDAR Range-View-based Moving Object Segmentation framework that segme