An Approach to Translate Synthetic Lidar Point Cloud Data Similar to Real Lidar Data and Evaluate Using Deep Learning Techniques-Reference-Cited by-同舟云学术

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An Approach to Translate Synthetic Lidar Point Cloud Data Similar to Real Lidar Data and Evaluate Using Deep Learning Techniques

Published:2023-12-01 Issue: Volume: Page:1-6
ISSN:
Container-title:2023 IEEE 3rd Mysore Sub Section International Conference (MysuruCon)
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Author:

Phanindra Kumar Matha Vijaya¹,Thayyilravi Arunkrishna²,Karthika R¹,Manoj C R³,Kumar Soni Rajat⁴

Affiliation:

1. Amrita School of Engineering Amrita Vishwa Vidyapeetham,Department of Electronics and Communication Engineering,Coimbatore,India

2. Tata Consultancy Services,Technical Architect Autonomous Driving,Bangalore,India

3. Tata Consultancy Services,Head Autonomous Driving,Bangalore,India

4. AI Developer Tata Consultancy Services,Bangalore,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10395909/10396851/10396953.pdf?arnumber=10396953

Reference30 articles.

1. A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation

2. Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection

3. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception

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

5. Towards synthesized training data for semantic segmentation of mobile laser scanning point clouds: Generating level crossings from real and synthetic point cloud samples

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