3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …

Semantic occupancy perception is essential for autonomous driving, as automated vehicles
require a fine-grained perception of the 3D urban structures. However, existing relevant …

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a
solution with simple configuration compared to typical multi-sensor systems. The main …

Existing top-performance 3D object detectors typically rely on the multi-modal fusion
strategy. This design is however fundamentally restricted due to overlooking the modality …

In this work, we present FFB6D, a full flow bidirectional fusion network designed for 6D pose
estimation from a single RGBD image. Our key insight is that appearance information in the …

In this work, we present a novel data-driven method for robust 6DoF object pose estimation
from a single RGBD image. Unlike previous methods that directly regressing pose …

We present AVOD, an Aggregate View Object Detection network for autonomous driving
scenarios. The proposed neural network architecture uses LIDAR point clouds and RGB …

Autonomous driving perception has made significant strides in recent years, but accurately
sensing the environment using a single sensor remains a daunting task. This review offers a …

Image-guided depth completion aims to generate dense depth maps with sparse depth
measurements and corresponding RGB images. Currently, spatial propagation networks …

Depth completion, the technique of estimating a dense depth image from sparse depth
measurements, has a variety of applications in robotics and autonomous driving. However …