Multi-sensor modal fusion has demonstrated strong advantages in 3D object detection tasks.
However, existing methods that fuse multi-modal features require transforming features into …

In this paper, we present RPF3D, an innovative single-stage framework that explores the
complementary nature of point clouds and range images for 3D object detection. Our …

Sensor fusion is an important method for achieving robust perception systems in
autonomous driving, Internet of things, and robotics. Most multi-modal 3D detection models …

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

In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Fusing the camera and LiDAR information has become a de-facto standard for 3D object
detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to …

An accurate and robust perception system is key to understanding the driving environment
of autonomous driving and robots. Autonomous driving needs 3-D information about objects …

In autonomous driving, cooperative perception makes use of multi-view cameras from both
vehicles and infrastructure, providing a global vantage point with rich semantic context of …

The fusion of multimodal sensor data streams such as camera images and lidar point clouds
plays an important role in the operation of autonomous vehicles (AVs). Robust perception …

Three-dimensional object detection plays a key role in autonomous driving systems. The
performance of light detection and ranging (LiDAR)-based models is limited because of the …