Improving single image localization through domain adaptation and large kernel attention with synthetic data-Reference-Cited by-同舟云学术

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Improving single image localization through domain adaptation and large kernel attention with synthetic data

Published:2024-11 Issue: Volume:137 Page:108951
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yao Dansheng,Zhu Hehua,Ren Bangke^ORCID,Zhuang Xiaoying^ORCID

Publisher

Elsevier BV

Reference44 articles.

1. BIM-PoseNet: indoor camera localisation using a 3D indoor model and deep learning from synthetic images;Acharya;ISPRS J. Photogrammetry Remote Sens.,2019

2. A recurrent deep network for estimating the pose of real indoor images from synthetic image sequences;Acharya;Sensors,2020

3. Modelling uncertainty of single image indoor localisation using a 3d model and deep learning;Acharya;ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.,2019

4. Single-image localisation using 3D models: combining hierarchical edge maps and semantic segmentation for domain adaptation;Acharya;Autom. ConStruct.,2022

5. Synthetic-real image domain adaptation for indoor camera pose regression using a 3D model;Acharya;ISPRS J. Photogrammetry Remote Sens.,2023

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