A fine virtual reconstruction algorithm for ancient buildings based on adaptive fusion model of multi angle LiDAR measurement data

Xiaoyun Liu

doi:10.1117/12.3007966

14 November 2023 A fine virtual reconstruction algorithm for ancient buildings based on adaptive fusion model of multi angle LiDAR measurement data

Xiaoyun Liu

Author Affiliations +

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341K (2023) https://doi.org/10.1117/12.3007966
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

Due to the inconsistent color tones of ancient buildings, the accuracy of model construction is low. In order to improve the fine reconstruction effect of ancient buildings, a multi-angle LiDAR measurement data adaptive fusion model for fine virtual reconstruction of ancient buildings is proposed. Obtain fine data through data collection, preprocess the fine data of the ancient building, set judgment conditions based on the processing results, extract fine feature information of the ancient building from multi angle LiDAR measurement data, and achieve fine virtual reconstruction of the ancient building. The experimental results show that the fine virtual reconstruction scene under the proposed algorithm is clear and complete, and the reconstruction results of the length, width, and height of ancient buildings are consistent with the actual measurement results, with high accuracy and good results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaoyun Liu "A fine virtual reconstruction algorithm for ancient buildings based on adaptive fusion model of multi angle LiDAR measurement data", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341K (14 November 2023); https://doi.org/10.1117/12.3007966

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available