Towards an optimized paradigm: generative adversarial networks and 3D modeling in landscape design and generation
Ming He
TL;DR
This paper introduces a new VR-based method for urban landscape design that uses 3D scanning and generative networks to reduce rendering time and improve visual quality.
Contribution
A novel urban landscape design method combining 3D laser scanning, adversarial generative networks, and texture mapping to enhance efficiency and visual fidelity.
Findings
The proposed method reduces rendering times by up to 90% compared to traditional tools.
It achieves significant improvements in visual fidelity as measured by standard image quality metrics.
Performance assessments across four scenarios validate the method's effectiveness in urban landscape simulations.
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…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Urban Green Space and Health · 3D Surveying and Cultural Heritage