ArticlePloS one2025
Towards an optimized paradigm: generative adversarial networks and 3D modeling in landscape design and generation.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtual reality (VR) integrates technologies like computer graphics, artificial intelligence, and multi-sensor systems, creating transformative tools for designers and users. This study proposes a novel urban landscape design method using 3D laser scanning combined with frame reorganization and texture mapping. Despite the advancements in VR-based landscape design, existing methods often suffer from inefficiencies in rendering time and suboptimal visual fidelity, limiting their practical application in large-scale urban projects. In the initial phase, we acquire the central pixel point of the images via a meticulous 3D scanning process, thus facilitating a three-dimensional stereo reorganization of urban architectural landscapes. This stage is succeeded by the application of a terahertz wave image segmentation strategy, grounded in the sophisticated utilization of adversarial generative networks and a structured texture mapping procedure. This technique permits the virtual reconstruction of the architectural blueprint, wherein each image layer is systematically traversed, engendering a dynamic representation of the urban landscape. The final step generates realistic urban landscape simulations using integrated 3D laser scanning. To ascertain the efficacy of the proposed methodology, we embarked upon a series of performance assessments across four disparate simulation design scenarios, yielding verifiable outcomes. Our empirical findings demonstrate that the proposed method reduces rendering times by up to 90% compared to traditional tools like SketchUp and 3D Studio Max, while achieving a significant improvement in visual fidelity, as evidenced by standard image quality metrics. These results attest to the formidable potential of this avant-garde approach within the VR landscape design milieu, significantly diminishing the time imperative while augmenting visual fidelity and fortifying automatic display proficiencies. By virtue of its robust analytical underpinnings and innovative approach, this research furnishes a substantial theoretical scaffolding for the evolving discourse in landscape space design, prompting a reevaluation of conventional methodologies while propelling the field towards a more efficient and visually immersive future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.