Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks-Reference-Cited by-同舟云学术

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Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks

Published:2019-05 Issue: Volume: Page:
ISSN:
Container-title:2019 International Conference on Robotics and Automation (ICRA)
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Author:

Tu Chenxi,Takeuchi Eijiro,Carballo Alexander,Takeda Kazuya

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8780387/8793254/08794264.pdf?arnumber=8794264

Cited by 55 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. Entropy‐driven Progressive Compression of 3D Point Clouds;Computer Graphics Forum;2024-08

2. Integrated Sensing and Communications for 3D Object Imaging via Bilinear Inference;IEEE Transactions on Wireless Communications;2024-08

3. Impact of LiDAR point cloud compression on 3D object detection evaluated on the KITTI dataset;EURASIP Journal on Image and Video Processing;2024-06-17

4. Octree-Retention Fusion: A High-Performance Context Model for Point Cloud Geometry Compression;Proceedings of the 2024 International Conference on Multimedia Retrieval;2024-05-30

5. A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds;Sensors;2024-05-17

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