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Found 14 papers, 5 papers with code
A-Scan2BIM: Assistive Scan to Building Information Modeling
This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling (BIM) applications.
Representation Learning for Sequential Volumetric Design Tasks
We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation.
IKEA-Manual: Seeing Shape Assembly Step by Step
Human-designed visual manuals are crucial components in shape assembly activities.
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Layout design is an important task in various design fields, including user interface, document, and graphic design.
Translating a Visual LEGO Manual to a Machine-Executable Plan
We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions.
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
We present SkexGen, a novel autoregressive generative model for computer-aided design (CAD) construction sequences containing sketch-and-extrude modeling operations.
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
Structural Design Recommendations in the Early Design Phase using Machine Learning
In order to facilitate an informed exploration in the early design stage, we suggest the automation of fundamental structural engineering tasks and introduce ApproxiFramer, a Machine Learning-based system for the automatic generation of structural layouts from building plan sketches in real-time.
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects
This paper proposes a generative adversarial layout refinement network for automated floorplan generation.
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation
Volumetric design is the first and critical step for professional building design, where architects not only depict the rough 3D geometry of the building but also specify the programs to form a 2D layout on each floor.
House-GAN++: Generative Adversarial Layout Refinement Networks
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation.
Learning to simulate and design for structural engineering
However, structural engineers in practice often avoid optimization and compromise on a suboptimal design for the majority of buildings, due to the large size of the design space, the iterative nature of the optimization methods, and the slow simulation tools.
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture.
Unsupervised Image to Sequence Translation with Canvas-Drawer Networks
Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding.
