Abstract 3D object detection from point cloud data plays an essential role in autonomous
driving. Current single-stage detectors are efficient by progressively downscaling the 3D …

Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These
methods typically comprise two steps: 1) Utilize a region proposal network to propose a …

Abstract 3D object detection in point clouds is important for autonomous driving systems. A
primary challenge in 3D object detection stems from the sparse distribution of points within …

Quantization-based methods are widely used in LiDAR points 3D object detection for its
efficiency in extracting context information. Unlike image where the context information is …

Accurate 3D object detection from point clouds is critical for autonomous vehicles. However,
point cloud data collected by LiDAR sensors are inherently sparse, especially at long …

Real-time 3D object detection is crucial for autonomous cars. Achieving promising
performance with high efficiency, voxel-based approaches have received considerable …

LiDAR-based 3D object detection pushes forward an immense influence on autonomous
vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected …

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 …

Recently, directly detecting 3D objects from 3D point clouds has received increasing
attention. To extract object representation from an irregular point cloud, existing methods …

Though 3D object detection from point clouds has achieved rapid progress in recent years,
the lack of flexible and high-performance proposal refinement remains a great hurdle for …