Comparative analysis of text-To-3D AI tools in urban furniture design: Evaluating Luma Genie, Meshy, Tripo, and DeepAI

This study explores the potential of text-to-3D model AIs in designing urban furniture, with a focus on bus stops, street lamps, and benches, and provides a comparative evaluation of four prominent AI tools: Luma Genie, Meshy, Tripo, and Deepai. A diverse poll of architects, urban planners, industrial designers, and students assessed the outputs based on key criteria: Aesthetic appeal, texture detail, form detail, and technical consistency and feasibility. The comparative analysis revealed that Meshy consistently outperformed the other platforms across all criteria, achieving the highest overall score of 4.09. Meshy's success is attributed to its high performance in visual creativity, structural sophistication, and spatial awareness. Conversely, Deepai lagged significantly, notably lacking in functional logic, spatial awareness, and technical consistency, resulting in the lowest overall score of 1.69. While Luma Genie and Tripo showed balanced performance, they did not match Meshy's degree of structural and aesthetic intricacy. This study highlights the current limitations of text-to-3D AI, emphasizing that platform-specific features like customization and technical control play a critical role in generating feasible architectural outputs for the future of urban design.