Generating 3D House Wireframes with Semantics
Xueqi Ma, Yilin Liu, Wenjun Zhou, Ruowei Wang, Hui Huang

TL;DR
This paper introduces a novel autoregressive approach for generating semantically enriched 3D house wireframes, improving coherence and semantic integration over traditional methods.
Contribution
It proposes a unified wire-based representation and a two-phase model combining a graph autoencoder with a transformer decoder for semantic-aware 3D wireframe generation.
Findings
Outperforms existing models in accuracy and semantic fidelity
Produces detailed, component-segmented wireframes
Validates effectiveness on a comprehensive house dataset
Abstract
We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house…
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Taxonomy
Topics3D Surveying and Cultural Heritage · 3D Shape Modeling and Analysis · Image Processing and 3D Reconstruction
