PROJECTAI

Floorplan AI

Turn any floor plan—scanned, sketched, or digital—into structured data in seconds.

100,000+

<4sec

>20types

YEAR
2025
TEAM
Syed Shaaz · Aashwin Shrivastava
TECH-STACK
Vision Transformers
LOCATION
Germany
PUBLISHED
Aug 14, 2025

Project Overview

Floorplan AI, Figure 2AI-GENERATED
Floorplan AI, Figure 2

Objective: 


The aim of this project was to analyze the inputted floorplans in pdf or image formats and extract key information from these floorplans. From the inputted floorplan, we had to extract the shape of the various rooms present in the image and determine the coordinates of the rooms present in the image. 



Our Solution:

A multi-stage vision pipeline combining transformer-based segmentation (Vision Transformers), edge/line extraction, corner detection & geometric post-processing to produce clean room polygons.


Challenges Overcome:

  • Handling vast variability in styles, annotations, resolutions, and languages.
  • Creating an efficient annotation process under tight deadlines.
  • Combining multiple AI pipelines (vision transformers, noise reduction, edge detection) for precise results.
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Business Value & Applications:

  1. Real Estate: Automatically generate interactive property listings.
  2. Construction: Reduce manual CAD drafting from days to minutes.
  3. Interior Design: Quickly create scalable room layouts for proposals.
  4. Property Management: Track and manage facility layouts for renovations.
Floorplan AI, Figure 7

Project Insights

This project elegantly highlights various use cases and possibilities that could be achieved for various domains and use cases:

  1. Vision Transformer models can be combined together and trained about how a certain characteristic in any image generally looks like and be used to reliably detect and classify certain key characteristics. Even complex characteristics like shapes of objects can be detected.
  2. Even if there is significance variation in how a potential object/ archetype/ property or characteristic in an image could look like, and there is significant variation in the input images that are provided by users, AI can be trained to reliably provide relevant solutions.

  3. Even Lengths and Open shapes like fences can be detected. However, the quality of results are strongly influences by the quality and quantity of input data, clarity of scope, availability of research budget, consistency and resolution of images under consideration.
Floorplan AI, Figure 8

Further Possibilities

  1. Integration with AR/VR tools for immersive walkthroughs.
  2. AI-based renovation cost estimation from floorplans.
  3. Automated compliance checking with building regulations.
Floorplan AI, Figure 9
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Alan Kay

“The best way to predict the future is to invent it.”