D3GATTEN: Dense 3D Geometric Features Extraction and Pose Estimation Using Self-Attention-Reference-Cited by-同舟云学术

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D3GATTEN: Dense 3D Geometric Features Extraction and Pose Estimation Using Self-Attention

Published:2023 Issue: Volume:11 Page:7947-7958
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kelenyi Benjamin¹^ORCID,Tamas Levente¹^ORCID

Affiliation:

1. Automation Department, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Funder

Romanian National Authority for Scientific Research, CNCS-UEFISCDI

European Union’s Horizon 2020 Research and Innovation Program

Project Network of Excellence in Applied Research and Innovation for Doctoral and Postdoctoral Programs/InoHubDoc

European Social Fund

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/ielx7/6287639/10005208/10024269.pdf?arnumber=10024269

Reference57 articles.

1. CoFiNet: Reliable coarse-to-fine correspondences for robust point cloud registration;yu;Proc Adv Neural Inf Process Syst,2021

2. 3D Point Capsule Networks

3. Color Point Cloud Registration Based on Supervoxel Correspondence

4. 3D Local Features for Direct Pairwise Registration

5. PREDATOR: Registration of 3D Point Clouds with Low Overlap

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. SAM-Net: Self-Attention based Feature Matching with Spatial Transformers and Knowledge Distillation;Expert Systems with Applications;2024-05

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