As the foundation of intelligent systems, machine vision perceives the surrounding
environment and provides a basis for decision-making. Object detection is the core task in …

Current 3D object detection models follow a single dataset-specific training and testing
paradigm, which often faces a serious detection accuracy drop when they are directly …

Abstract 3D object detection on LiDAR–camera system is a challenging task, for 3D LiDAR
point and 2D RGB image have different data representation. In this paper, We consider that …

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-based 3D object detection pushes forward an immense influence on autonomous
vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected …

LiDAR-Camera fusion-based 3D detection is a critical task for automatic driving. In recent
years, many LiDAR-Camera fusion approaches sprung up and gained promising …

We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous
methods often make domain-specific design decisions, for example projecting points into a …

LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging
from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been …

LiDAR-produced point clouds are the major source for most state-of-the-art 3D object
detectors. Yet, small, distant, and incomplete objects with sparse or few points are often hard …

Most state-of-the-art 3D object detectors rely heavily on LiDAR sensors and there remains a
large gap in terms of performance between image-based and LiDAR-based methods …