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

Sensor fusion is an essential topic in many perception systems, such as autonomous driving
and robotics. Existing multi-modal 3D detection models usually involve customized designs …

Camera and LiDAR sensor modalities provide complementary appearance and geometric
information useful for detecting 3D objects for autonomous vehicle applications. However …

Multi-modal fusion is a basic task of autonomous driving system perception, which has
attracted many scholars' attention in recent years. The current multi-modal fusion methods …

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits
feature fusion from both modalities for point-wise scene flow estimation. Previous methods …

Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact
with the world. RGB cameras record 2D images with rich semantic information while missing …

Pixel image data of a scene is received in which the pixel image data includes a two-
dimensional representation of an object in the scene. Point cloud data including three …

RGB cameras and LiDAR are crucial sensors for autonomous vehicles that provide
complementary information for accurate detection. Recent early-level fusion-based …

Vehicle speed estimation is one of the most critical issues in intelligent transportation system
(ITS) research, while defining distance and identifying direction have become an …

Autonomous driving has become a prominent topic with the rise of intelligent urban vision in
communities. Advancements in automated driving technology play a significant role in the …