LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

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 …

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 …

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 …

Autonomous driving perceives its surroundings for decision making, which is one of the most
complex scenarios in visual perception. The success of paradigm innovation in solving the …

Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …

Monocular 3D object detection is an important task for autonomous driving considering its
advantage of low cost. It is much more challenging than conventional 2D cases due to its …

In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to
input scene size is significantly smaller compared to 2D detection cases. Overlooking this …

Abstract 3D object detection in autonomous driving aims to reason “what” and “where” the
objects of interest present in a 3D world. Following the conventional wisdom of previous 2D …

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …