3DYOLO: Real-time 3D Object Detection in 3D Point Clouds for Autonomous Driving-Reference-Cited by-同舟云学术

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3DYOLO: Real-time 3D Object Detection in 3D Point Clouds for Autonomous Driving

Published:2021-12-06 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS)
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Author:

V Priya M¹,Pankaj Dhanya S¹

Affiliation:

1. College of Engineering Trivandrum,Department of Computer Science and Engineering,Trivandrum,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9791458/9791465/09791912.pdf?arnumber=9791912

Reference24 articles.

1. R-fcn: Object detection via region-based fully convolutional networks;li;NIPS,2016

2. SSD: Single Shot MultiBox Detector

3. What could move? Finding cars, pedestrians and bicyclists in 3D laser data;wang;2012 IEEE International Conference on Robotics and Automation,2012

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Point Cloud-Based Real-Time 3D Object Detection for Predictive Analytics of Safety Incidents in Manufacturing Industry;2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP);2023-11-21

2. BEV Approach Based Efficient Object Detection using YoloV4 for LiDAR Point Cloud;2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring);2023-06

3. A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving;Sensors;2022-12-07

4. Deep Learning for Clothing Style Recognition Using YOLOv5;Micromachines;2022-10-05

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