Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Radar is a key component of the suite of perception sensors used for safe and reliable
navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity …

LiDAR-camera fusion methods have shown impressive performance in 3D object detection.
Recent advanced multi-modal methods mainly perform global fusion, where image features …

In this paper, we present a neat yet effective transformer-based framework for visual
grounding, namely TransVG, to address the task of grounding a language query to the …

Autonomous vehicles require accurate, and fast decision-making perception systems to
know the driving environment. The 2D object detection is critical in allowing the perception …

It has been well recognized that fusing the complementary information from depth-aware
LiDAR point clouds and semantic-rich stereo images would benefit 3D object detection …

How to ensure robust and accurate 3D object detection under various environment is
essential for autonomous driving (AD) environment perception. While, until now, most of the …

3D object detection from multiple image views is a fundamental and challenging task for
visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object …

LiDAR and cameras are complementary sensors for 3D object detection in autonomous
driving. However, it is challenging to explore the unnatural interaction between point clouds …

Multi-modal 3D object detection has been an active research topic in autonomous driving.
Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D …