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 …

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 …

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 …

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities
with complementary cues for 3D object detection. However, it is quite difficult to sufficiently …

Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding.
However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5 D as they …

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 …

Three-dimensional object detection from light detection and ranging (LiDAR) point clouds
has gained great attention in recent years due to its wide applications in smart cities and …

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 …

Detecting objects from LiDAR point clouds is of tremendous significance in autonomous
driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved …