LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

Nowadays, vehicles with a high level of automation are being driven everywhere. With the
apparent success of autonomous driving technology, we keep working to achieve fully …

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail
to capture the complete spatial information about the objects due to long distance and …

Point clouds have been a popular representation to describe 3D environments for
autonomous driving applications. Despite accurate depth information, sparsity of points …

Point cloud upsampling is a basic low-level task, that is important for improving the quality of
a point cloud. However, existing point cloud upsampling methods perform poorly on sparse …

Recent advancements in vision-language pre-training (eg, CLIP) have enabled 2D vision
models to benefit from language supervision. However, the joint representation learning of …

This paper investigates the semantic segmentation problem for large-scale point clouds.
Recent segmentation methods usually employ an encoder–decoder architecture. However …

LiDAR, as an excellent sensor, can provide positions, motion states, and other objective
attribute information of objects in the 3D world. Inevitably, the inherent sparsity of point cloud …

Recently, 3D object detection from LiDAR point clouds has advanced rapidly. Although the
second stage can improve the detection performance significantly, prior works concern little …

The use of 3D information in industrial anomaly detection tasks has been shown to enhance
performance by uncovering unseen abnormal patterns in the RGB modality. Despite the …