A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

IoU-uniform R-CNN: Breaking through the limitations of RPN

(2021) Li Zhu et al.   PATTERN RECOGNITION

Voxel-FPN: Multi-Scale Voxel Feature Aggregation for 3D Object Detection from LIDAR Point Clouds

(2020) Hongwu Kuang et al.   SENSORS

Deep 3D Object Detection Networks Using LiDAR Data: A Review

(2020) Yutian Wu et al.   IEEE SENSORS JOURNAL

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

(2020) Duarte Fernandes et al.   Information Fusion

Dynamic Graph CNN for Learning on Point Clouds

(2019) Yue Wang et al.   ACM TRANSACTIONS ON GRAPHICS

A Survey on 3D Object Detection Methods for Autonomous Driving Applications

(2019) Eduardo Arnold et al.   IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection

(2019) Yangyang Ye et al.   NEUROCOMPUTING

3D object detection based on sparse convolution neural network and feature fusion for autonomous driving in smart cities

(2019) Lei Wang et al.   Sustainable Cities and Society

SECOND: Sparsely Embedded Convolutional Detection

(2018) Yan Yan et al.   SENSORS

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

The Graph Neural Network Model

(2008) F. Scarselli et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Autonomous driving in urban environments: Boss and the Urban Challenge

(2008) Chris Urmson et al.   Journal of Field Robotics