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Turning 2D floor plans into 3D-ready JSON with Detectron2

Turning 2D floor plans into 3D-ready JSON with Detectron2

by acaciabengo·Feb 26, 2026·2 points·0 comments

AI Analysis

●●SolidWizardryNiche Gem

Instance segmentation for floor plans—clever CV work, but niche audience and no monetization path.

Strengths
  • Pixel-perfect masking approach avoids manual data entry bottleneck for 3D modeling pipelines.
  • Runs full inference on CPU in <10s, making it practical for real product integration.
  • Trained on real-world highly occluded floor plans, not toy datasets.
Weaknesses
  • 38.2% AP0.50:0.95 is respectable but not production-grade for real architectural workflows.
  • Demo-only status—no code, no pretrained weights, no clear commercialization plan.
Category
Target Audience

PropTech startups, architectural visualization platforms, interior design software

Post Description

Hey HN,

For the past few weekends, I have been working on a computer vision pipeline to solve a specific PropTech problem: turning messy, highly occluded 2D floor plans into clean, structured data for 3D extrusion. I originally built this as a demo for a firm.

The Stack & Architecture I built an instance segmentation pipeline that relies strictly on pixel-perfect masking to extract the geometry.

The Backbone: Swin Transformer + Detectron2 + OpenCV Training: Trained on 1024x1024 images using an RTX 4090. Inference: Runs on CPU in < 10s. Demo Performance: 67.1% AP50 for instance segmentation masks, and 38.2% AP across the strict 0.50:0.95 IoU thresholds.

Why I'm posting: A lot of virtual staging and architectural startups have beautiful Three.js rendering engines, but still rely on manual data entry to build the base geometry. I built this specifically as an extraction engine to sit underneath those UIs.

If you are in PropTech or building a product that could benefit from embedding this model under the hood, I would love to chat.

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