Autonomous driving vehicles require both a precise localization and map** solution in
different driving environment. In this context, Simultaneous Localization and Map** …

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

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which
converges faster and better suits modern image backbones. Existing state-of-the-art BEV …

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 Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses
RGB images and LiDAR data by depth completion has gained great attention. However …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

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 …

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 …

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 …

How should we integrate representations from complementary sensors for autonomous
driving? Geometry-based fusion has shown promise for perception (eg, object detection …