2 citations · 4 across the 13 of their papers we have counts for
14 papers
CrossProjection: Geometric Grounding Beyond Viewpoint Change in Architectural Drawings
Kaho Li, Pengyu Zeng, Yuqin Dai +3
Architectural drawings violate the usual assumption behind multi-view reasoning: plans and sections are cuts, while elevations are facade projections, so corresponding components c…
PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation
Pengyu Zeng, Yuqin Dai, Jun Yin +7
Two structural insights have been overlooked in automated residential floor plan generation. First, design is inherently progressive. Architects begin with rough strokes and refine…
Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
Haoyuan Sun, Jing Wang, Yuxin Song +9
Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as the robust paradigm for furth…
GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework
Pengyu Zeng, Yuqin Dai, Jun Yin +7
Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable.…
EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes
Yuqin Dai, Guoqing Wang, Yuan Wang +13
Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging externa…
FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction
Jun Yin, Pengyu Zeng, Jing Zhong +4
In the architectural design process, floor plan generation is inherently progressive and iterative. However, existing generative models for floor plans are predominantly end-to-end…
Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance
Peilin Li, Jun Yin, Jing Zhong +3
In the context of the digital development of architecture, the automatic segmentation of walls and windows is a key step in improving the efficiency of building information models…
FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs
Jun Yin, Jing Zhong, Pengyu Zeng +4
In the architectural design process, floorplan design is often a dynamic and iterative process. Architects progressively draw various parts of the floorplan according to their idea…
ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models
Jing Zhong, Jun Yin, Peilin Li +4
Architectural cultures across regions are characterized by stylistic diversity, shaped by historical, social, and technological contexts in addition to geograph-ical conditions. Un…
Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges.…
ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes
Jun Yin, Jing Zhong, Pengyu Zeng +4
In contemporary architectural design, the growing complexity and diversity of design demands have made generative plugin tools essential for quickly producing initial concepts and…
UrbanSense:A Framework for Quantitative Analysis of Urban Streetscapes leveraging Vision Large Language Models
Jun Yin, Jing Zhong, Peilin Li +4
Urban cultures and architectural styles vary significantly across cities due to geographical, chronological, historical, and socio-political factors. Understanding these difference…
PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net
Jun Yin, Yangfan He, Miao Zhang +4
Learning and improving large language models through human preference feedback has become a mainstream approach, but it has rarely been applied to the field of low-light image enha…
TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement
Miao Zhang, Jun Yin, Pengyu Zeng +3
Deep learning-based image enhancement methods show significant advantages in reducing noise and improving visibility in low-light conditions. These methods are typically based on o…