In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

In the near future, autonomous vehicles with full self-driving features will populate our public
roads. However, fully autonomous cars will require robust perception systems to safely …

Abstract 3D scene understanding plays a vital role in vision-based autonomous driving.
While most existing methods focus on 3D object detection, they have difficulty describing …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

Abstract Recently, the Segment Anything Model (SAM) emerged as a powerful vision
foundation model which is capable to segment anything in 2D images. This paper aims to …

State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …

In this paper, we propose an anchor-free single-stage LiDAR-based 3D object detector--
RangeDet. The most notable difference with previous works is that our method is purely …

Autonomous robotic systems and self driving cars rely on accurate perception of their
surroundings as the safety of the passengers and pedestrians is the top priority. Semantic …

LiDAR-based 3D detection in point cloud is essential in the perception system of
autonomous driving. In this paper, we present LiDAR R-CNN, a second stage detector that …

Accurate detection of obstacles in 3D is an essential task for autonomous driving and
intelligent transportation. In this work, we propose a general multimodal fusion framework …