Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

With the development of society, technological progress, and new needs, autonomous
driving has become a trendy topic in smart cities. Due to technological limitations …

We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …

Query-based transformer has shown great potential in constructing long-range attention in
many image-domain tasks, but has rarely been considered in LiDAR-based 3D object …

Real-time and high-performance 3D object detection is of critical importance for autonomous
driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel …

There have been two streams in the 3D detection from point clouds: single-stage methods
and two-stage methods. While the former is more computationally efficient, the latter usually …

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

Monocular 3D detection has drawn much attention from the community due to its low cost
and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space …

The 3-D object detection with light detection and ranging (LiDAR) point clouds is a
challenging problem, which requires 3-D scene understanding, yet this task is critical to …

In this work, we introduce FARP-Net, an adaptive local-global feature aggregation and
relation-aware proposal network for high-quality 3D object detection from pure point clouds …