Light Detection and Ranging (LiDAR) technology has the advantages of high detection
accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is …

We address semantic segmentation of road-objects from 3D LiDAR point clouds. In
particular, we wish to detect and categorize instances of interest, such as cars, pedestrians …

LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In
contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an …

We introduce a statistical extension of the classic Poisson Surface Reconstruction algorithm
for recovering shapes from 3D point clouds. Instead of outputting an implicit function, we …

Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer
vision, where state-of-the-art methods are mainly based on full supervision. As annotating …

The problem of traffic safety has become increasingly prominent owing to the increase in the
number of cars. Traffic accidents often occur in an instant, which makes it necessary to …

High-resolution micro traffic data are important to traffic safety and efficiency analysis. In this
study, a roadside LiDAR sensor is used to collect 3D point clouds of surrounding objects. An …

Abstract 3D instance segmentation (3DIS) is a crucial task but point-level annotations are
tedious in fully supervised settings. Thus using bounding boxes (bboxes) as annotations has …

Object segmentation for 3-D point clouds plays a critical role in autonomous driving, robotic
navigation, and other computer version applications. In object segmentation, all points are …

In this paper, we present a driverless taxi system for autonomous campus mobility services.
College campuses have unique mobility requirements in terms of layout, population, and …