Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

2020-05-20 · Ying Li, Lingfei Ma, Zilong Zhong, Fei Liu, Dongpu Cao, Jonathan Li, Michael A. Chapman

Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on deep learning applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this paper is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3D deep architectures, the remarkable deep learning applications in 3D semantic segmentation, object detection, and classification; specific datasets, evaluation metrics, and the state of the art performance. Finally, we conclude the remaining challenges and future researches.

📄 PDF Abstract BibTeX arXiv:2005.09830

Code (0)

등록된 구현이 없습니다.

Tasks

3D Semantic Segmentation Autonomous Driving Autonomous VehiclesDeep LearningGeneral Classification object-detection Object Detection Segmentation Semantic SegmentationSurvey

3D Point Cloud Processing and Learning for Autonomous Driving

2020-03-01 · Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez 외

We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles, light detection and ranging (LiDAR) sensors collect 3D point clouds that …

Autonomous Driving Autonomous Vehicles

A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving

2026-02-15 · June Moh Goo, Zichao Zeng, Jan Boehm arxiv

LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point clouds that miss critical details. LiDAR supe…

Autonomous Driving Model Compression Point Clouds

NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields

2023-04-28 · Junge Zhang, Feihu Zhang, Shaochen Kuang, Li Zhang

Labeling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms mo…

Autonomous Driving NeRF Novel View Synthesis Point Cloud Generation+1

Road Segmentation on low resolution Lidar point clouds for autonomous vehicles

2020-05-27 · Leonardo Gigli, B Ravi Kiran, Thomas Paul, Andres Serna 외

Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous dr…

Autonomous Driving Autonomous Vehicles Road Segmentation Segmentation

A Survey of Robust 3D Object Detection Methods in Point Clouds

2022-03-31 · Walter Zimmer, Emec Ercelik, Xingcheng Zhou, Xavier Jair Diaz Ortiz 외

The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges. We describe novel data augmentation methods, sampling strategies, activation functions, attent…

3D Object Detection Autonomous Driving Data Augmentation Object+3