Summary of Archcomplete: Autoregressive 3d Architectural Design Generation with Hierarchical Diffusion-based Upsampling, by S. Rasoulzadeh et al.
ArchComplete: Autoregressive 3D Architectural Design Generation with Hierarchical Diffusion-Based Upsampling
by S. Rasoulzadeh, M. Bank, I. Kovacic, K. Schinegger, S. Rutzinger, M. Wimmer
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel 3D generative model called ArchComplete is presented, which combines a vector-quantised model with an autoregressive transformer to generate coarse shapes, followed by hierarchical upsampling for fine structures and details. The pipeline learns a contextually rich codebook of local patch embeddings and optimises a 2.5D perceptual loss for global spatial correspondence. This allows ArchComplete to generate models at high resolutions (up to 512^{3}) with small voxel sizes (). The model achieves state-of-the-art performance in quality, diversity, and computational efficiency for tasks such as genetic interpolation, unconditional synthesis, shape completion, plan-drawing completion, and geometric detailisation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ArchComplete is a new way to generate 3D models. It’s like building with blocks, but instead of physical blocks, it uses math to create detailed shapes. The model can make buildings, houses, or even objects with small parts. It’s good at making things that look realistic and are similar to each other. |
Keywords
* Artificial intelligence * Autoregressive * Generative model * Transformer
