3D-MELL: Integrating Large Language Models and Point Cloud Data for Architectural Knowledge Representation
The rapid advancement of artificial intelligence, particularly large language models (LLMs), has opened new possibilities for comprehension, reasoning, and creative exploration across disciplines. In architecture and urban studies, point cloud data has become a critical digital resource due to its capacity to capture precise spatial and geometric information. This study addresses the gap between existing point cloud analysis methods and their limited applicability to direct scene design and semantic understanding, proposing a novel AI-driven framework to bridge this divide.
Limitations of Current Point Cloud Applications
Although prior research has extensively explored point cloud data for tasks such as semantic segmentation and target detection, the perceptual outputs of these approaches are often difficult to translate into meaningful design actions. The lack of semantic richness and contextual reasoning constrains their usability in architectural scene interpretation and creative workflows. This research identifies these limitations as a key motivation for integrating language-based intelligence with 3D spatial data.
Architecture of the 3D-MELL Network
The study introduces the 3D-MELL (3D Multi-Element Large Language) network architecture, designed to extend the capabilities of large-scale language models in processing and reasoning over 3D data. The architecture incorporates structured representations that allow LLMs to interpret spatial configurations, moving beyond purely textual or geometric inputs toward integrated semantic–spatial understanding.
Semantic Encoding Using Ancient Architectural Elements
Ancient architectural components are employed as the primary data source to construct a semantically rich training dataset. Each architectural element is assigned a unique ID attribute marker along with spatial relationship markers, enabling precise representation of both object properties and inter-element relationships. This encoding strategy allows the model to capture architectural semantics, hierarchy, and spatial logic within a 3D environment.
Distributed Training and Model Optimization
To enhance adaptability and scalability, the 3D-MELL framework employs a distributed training strategy. This approach allows the model to be fine-tuned for diverse downstream tasks using specific command inputs. Experimental results demonstrate favorable training performance and fitting outcomes, indicating the model’s robustness in handling complex 3D semantic reasoning tasks.
Interactive Interface and Knowledge Transmission
Beyond model development, the study presents a front-end interactive chat interface developed using HTML and CSS frameworks. This interface enables users to engage with the 3D-MELL system in an intuitive conversational manner, offering a novel pathway for transmitting, exploring, and generating architectural historical knowledge. The integration of AI, 3D data, and interactive visualization highlights new possibilities for digital heritage, education, and design research.
Architecture Engineers Awards
🔗 Nominate now! 👉 https://architectureengineers.com/award-nomination/?ecategory=Awards&rcategory=Awardee 🌐 Visit: architectureengineers.com 📩 Contact: contact@architectureengineers.com Get Connected Here: ***************** Instagram : https://www.instagram.com/architecture_engineers_awards/ Facebook : https://www.facebook.com/profile.php?id=61576995475934 Tumblr : https://www.tumblr.com/architectureengineersawards Pinterest : https://in.pinterest.com/architectureengineersawards/ Blogger : https://architectureengineers.blogspot.com/ Twitter : https://x.com/Architectu54920 YouTube : https://www.youtube.com/@Architechtureengineer LinkedIn : https://www.linkedin.com/in/architecture-engineer-01a044361/
Comments
Post a Comment