Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge-Reference-Cited by-同舟云学术

Synthetic LiDAR-Labeled Data Set Generation to Train Deep Neural Networks for Object Classification in IoT at the Edge

Published:2022-12-15 Issue:24 Volume:9 Page:24812-24821
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wisultschew Cristian¹^ORCID,Hernandez Rogelio¹^ORCID,Pastor Carlos¹,Portilla Jorge¹^ORCID

Affiliation:

1. Centro de Electrónica Industrial, Universidad Politécnica de Madrid, Madrid, Spain

Funder

InSecTT European Project. InSecTT (www.insectt.eu) has received funding from the ECSEL Joint Undertaking

European Union’s Horizon 2020 Research and Innovation Programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, The Netherlands, and Turkey

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9973413/09844155.pdf?arnumber=9844155

Reference32 articles.

1. SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving

2. A LiDAR Point Cloud Generator

3. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

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2. An Approach to Translate Synthetic Lidar Point Cloud Data Similar to Real Lidar Data and Evaluate Using Deep Learning Techniques;2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon);2023-12-01