3D object detection is a crucial problem in environmental perception for autonomous driving.
Currently, most works focused on LiDAR, camera, or their fusion, while very few algorithms …

LiDAR devices are widely used in autonomous driving scenarios and researches on 3D
point cloud achieve remarkable progress over the past years. However, deep learning …

Accurate detection of obstacles in 3D is an essential task for autonomous driving and
intelligent transportation. In this work, we propose a general multimodal fusion framework …

Multi-modal 3D object detection has been an active research topic in autonomous driving.
Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D …

3D object detection is a key perception component in autonomous driving. Most recent
approaches are based on LiDAR sensors only or fused with cameras. Maps (eg, High …

Camera-LiDAR 3D object detection has been extensively investigated due to its significance
for many real-world applications. However, there are still of great challenges to address the …

Accurately detect vehicles or pedestrians from 3D point clouds (3D object detection) is a fast
developing research topic in autonomous driving and other domains. The fundamental …

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

We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and
radars in different combinations for 3D object detection. Specialized feature extractors take …

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 …