ArchiDiff: Advancing AI-Driven 3D Reconstruction for Architectural Design
ArchiDiff represents a significant advancement in AI-driven architectural visualization by addressing the long-standing limitations of 3D reconstruction from 2D images. While traditional methods perform adequately with small or simple scenes, they often struggle to handle complex, large-scale architectural forms. ArchiDiff overcomes these challenges by integrating a curated architectural dataset, diffusion-based 3D generation, and interactive design tools that support early-stage architectural workflows. This platform enables architects to seamlessly convert images into accurate point-cloud forms and modify them instantly using intuitive 2D interactions.
Architectural Design Challenges in Image-Based 3D Reconstruction
3D reconstruction from images in architecture remains limited due to occlusions, irregular geometries, and dense urban environments that complicate visual interpretation. Existing reconstruction pipelines typically fail to generalize to buildings with complex facades, multi-layered structures, or large contextual backgrounds. By positioning itself specifically for architectural use-cases, ArchiDiff directly confronts these shortcomings and provides a tailored solution for the demands of early-stage design, where rapid and reliable form visualization is essential.
Development of Archi Cloudnet: A Specialized Architectural Dataset
A major component of ArchiDiff’s contribution is the creation of Archi Cloudnet, a dataset intentionally curated for architectural form generation. This dataset captures a wide spectrum of building types, geometric complexities, and design styles, enabling robust training of AI models in contexts where standard datasets fall short. By focusing on architectural semantics and structural diversity, Archi Cloudnet ensures that the generated point clouds are more contextually relevant and spatially accurate for design applications.
Diffusion-Based 3D Generation Using Conditional Denoising Models
ArchiDiff employs a conditional denoising diffusion model to convert 2D images into 3D point-cloud representations. This approach allows the system to progressively refine architectural geometry while maintaining structural consistency across complex scenes. By integrating arbitrary object segmentation models into the diffusion framework, ArchiDiff enhances recognition accuracy and effectively filters out irrelevant background elements, resulting in clearer, more precise architectural outputs suitable for design workflows.
Interactive 2D-to-3D Image Editing for Early-Stage Design
One of ArchiDiff’s defining innovations is its interactive editing feature, which enables designers to adjust architectural forms directly through 2D drag-and-drop interactions. These modifications are instantly reflected in the 3D output, creating a dynamic feedback loop between sketching and spatial visualization. This real-time interaction aligns with early-stage design practices, empowering architects to iterate rapidly, explore multiple form variations, and maintain creative control throughout the conceptual process.
Experimental Evaluation and Real-World Validation
To assess its performance, ArchiDiff was evaluated using three datasets: Archi Cloudnet, RealCity3D, and Building Net. The results demonstrated superior generation accuracy compared to state-of-the-art baselines, particularly in scenarios involving complex spatial layouts and background noise. Additionally, tests conducted using real architectural sketches confirmed ArchiDiff’s suitability for early-stage design tasks, highlighting its ability to handle freehand inputs while producing structurally reliable point-cloud forms.
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