Deep Learning Based Point Cloud Processing Techniques
Author:
Affiliation:
1. Graduate School of Natural and Applied Sciences, Ankara Yıldırım Beyazıt University, Ankara, Turkey
2. Department of Computer Engineering, Ankara Yıldırım Beyazıt University, Ankara, Turkey
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09968224.pdf?arnumber=9968224
Reference176 articles.
1. Leveraging Shape Completion for 3D Siamese Tracking
2. FlowNet3D: Learning Scene Flow in 3D Point Clouds
3. Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
4. PillarFlowNet: A Real-time Deep Multitask Network for LiDAR-based 3D Object Detection and Scene Flow Estimation
5. FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation
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1. Deep learning for 3D object recognition: A survey;Neurocomputing;2024-12
2. Hybrid and Efficient Neural Network Design for LiDAR Point Cloud Data Processing;2023 IEEE 20th India Council International Conference (INDICON);2023-12-14
3. Depthwise Over-Parameterized CNN for Voxel Human Pose Classification;2023 International Seminar on Intelligent Technology and Its Applications (ISITIA);2023-07-26
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