Starting from Non-Parametric Networks for 3D Point Cloud Analysis-Reference-Cited by-同舟云学术

Starting from Non-Parametric Networks for 3D Point Cloud Analysis

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
Short-container-title:

Author:

Zhang Renrui¹,Wang Liuhui²,Wang Yali³,Gao Peng⁴,Li Hongsheng¹,Shi Jianbo⁵

Affiliation:

1. CUHK MMLab

2. Peking University

3. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences

4. Shanghai Artificial Intelligence Laboratory

5. University of Pennsylvania

Funder

National Key R&D Program of China

Innovation and Technology Commission (ITC)'s InnoHK

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203175.pdf?arnumber=10203175

Reference44 articles.

1. Pointcnn: Convolution on x-transformed points;li;Advances in neural information processing systems,2018

2. Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling;wu;Proceedings of the 30th International Confer-ence on Neural Information Processing Systems,0

3. Pedestrian recognition using high-definition LIDAR

4. Dynamic Graph CNN for Learning on Point Clouds

5. Relation-Shape Convolutional Neural Network for Point Cloud Analysis

Cited by 5 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. Deep Scene Flow Learning: From 2D Images to 3D Point Clouds;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-01

2. Point Cloud Classification via Learnable Memory Bank;MultiMedia Modeling;2024

3. A lightweight point cloud analysis network based on graph walking;2023 8th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS);2023-11-23

4. ASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields;2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW);2023-10-02

5. EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06