| id |
caadria2024_114 |
| authors |
Yin, Jun, Xu, Pengjian, Gao, Wen, Zeng, Pengyu and Lu, Shuai |
| year |
2024 |
| title |
Drag2build: Interactive Point-Based Manipulation of 3D Architectural Point Clouds Generated From a Single Image |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.169
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 169–178 |
| summary |
At present, 3D reconstruction from images has made notable advancements in simple, small-scale scenes, but faces significant challenges in intricate, expansive architectural scenes. Focusing on the early stage of design stage, we present Drag2Build, a tool for converting images into point clouds for 3D reconstruction and modification in detailed architectural contexts. Our first step involved the creation of ArchiNet, a specialized 3D reconstruction dataset dedicated to elaborate architectural scenes. Next, we developed a 3D reconstruction approach using a conditional denoising diffusion model, enhanced by incorporating a model for segmenting objects, thereby improving segmentation and identification in complex scenes. Additionally, our system features an interactive component that allows for immediate modification of 2D images via an easy drag-and-drop action, synchronously updating 3D architectural point clouds. The performance of Drag2Build in 3D reconstruction precision was assessed and benchmarked against mainstream methods using ArchiNet. The experiments showed that our approach is capable of producing high-quality 3D point clouds, facilitating swift editing and efficient handling of intricate backgrounds. |
| keywords |
3D building Generation, Diffusion Model, Single Image Reconstruction, DragDiffusion |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (2,637,718 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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