Abstract: To achieve better digital landscape design and visual presentation effects, this study proposes a digital landscape design method based on improved 3D image reconstruction technology. Firstly, a precise point cloud registration algorithm combining normal distribution transformation and Trimmed iterative nearest point algorithm is proposed. A color texture method for 3D models is designed in terms of 3D reconstruction, and a visual scene, 3D reconstruction method based on RGBD data is constructed. Secondly, knowledge networks are introduced to assist in the intelligent generation and planning of plant communities in urban landscape scenes. The knowledge network established through the plant database integrates the principles of landscape design and optimizes the layout of landscape plants in urban parks. The running speed and accuracy of research algorithms were superior to traditional methods, especially in terms of registration performance. Compared to the other two algorithms, the registration time of the research algorithm was reduced by 2%, and the errors were reduced by 71.4% and 87.5%, respectively. The panoramic quality of research methods fluctuated within a small range of 0.8 or above, while traditional methods exhibited instability and lower quality. The landscape design generated by research methods was more aesthetically pleasing and harmonious with the actual landscape in terms of plant selection and layout. The proposed method follows the principles of eco-friendly design and demonstrates significant potential for application in the field of urban landscape design.

Chen Chen, “Digital Landscape Architecture Design Combining 3D Image Reconstruction Technology” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150815

@article{Chen2024,
title = {Digital Landscape Architecture Design Combining 3D Image Reconstruction Technology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150815},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150815},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Chen Chen}
}