POPDNet: Primitive Object Pose Detection Network Based on Voxel Data with Three Cartesian Channels-Reference-Cited by-同舟云学术

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POPDNet: Primitive Object Pose Detection Network Based on Voxel Data with Three Cartesian Channels

Published:2021-12-29 Issue: Volume: Page:
ISSN:
Container-title:2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)
language:
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Author:

Makki Alireza¹,Hadi Alireza¹,Tarvirdizadeh Bahram¹,Teimouri Mehdi²

Affiliation:

1. University of Tehran,Faculty of New Sciences and Technologies,Dept. of Mechatronics Engineering,Tehran,Iran

2. University of Tehran,Faculty of New Sciences and Technologies,Dept. of Network Science and Technology,Tehran,Iran

Funder

University of Tehran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9729328/9729329/09729364.pdf?arnumber=9729364

Reference19 articles.

1. A Review on Deep Learning Techniques for 3D Sensed Data Classification

2. Multimodal deep learning for robust RGB-D object recognition

3. Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge

4. VoxNet: A 3D Convolutional Neural Network for real-time object recognition

5. Volumetric and Multi-view CNNs for Object Classification on 3D Data

Cited by 1 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. Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects;BMC Research Notes;2022-07-28

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