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 …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with …

LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …

Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …

In this work, we present a unified framework for multi-modality 3D object detection, named
UVTR. The proposed method aims to unify multi-modality representations in the voxel space …

Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make
contribution to the task of 3D object detection in different ways. Existing basic components in …

LiDAR-based 3D point cloud recognition has benefited various applications. Without
specially considering the LiDAR point distribution, most current methods suffer from …

Lidar-based sensing drives current autonomous vehicles. Despite rapid progress, current
Lidar sensors still lag two decades behind traditional color cameras in terms of resolution …

Abstract We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate
and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both …