acaciabengo

I built QuantTakeoff and releasing v1.0 for validation: Input a construction PDF, get back takeoff report with wall lengths, areas, door/window counts and sizes 3D GLB of the building at real-world scale all under ~10 mins.

The pain it solves: Reduce time estimator to trace out elements either by hand or software and extract out reports.

Stack: Ensemble of computer vision tools (95%), VLM OCR (5%)

Hard parts that are working: Auto-find the plan page in a 200-sheet bid set (most stacks make you point at the right sheet manually). I am still manually annotating a new dataset for the new models. Holds up on noisy as-builts and hand-marked sheets that break OCR-first pipelines Post CV processing to optimize for usability. Pixel accuracy to measure elements including size / width of doors and windows.

Demo: https://youtu.be/fVy7tDFqR98

Particularly want feedback on: Real world estimators feedback on what could be better for the practice. What would make use this or what is missing?

P.S Live demo has been removed to manage compute costs from a few enthusiasts who burn through the HF spaces bill.

I have built a pipeline that takes construction documents as PDFs, extracts out the floor plans.

It extracts out BIM data from the plans at pixel accuracy producing data on walls, floors, windows and doors accurately.

It also generates 3D impressions and a brief 10s 3D video of the floor plans.

I have had multiple discussion to build within a company already solving the same problem but not closed a deal yet and considering taking it to the market as a solo founder.

I am outside the UK and USA and was seeking a founder for market access and future fundraising.

What is the best advice on how to go to market as a sole founder?

Hey HN,

I am an ML Engineer and a full-stack software engineer. For the past few weekends, I have been working on a pipeline to solve a PropTech problem: turning messy, highly occluded 2D floor plans into clean, structured data for 3D extrusion. Originally demoed for a firm hiring for the role.

The Problem: If you try to use standard object detection (bounding boxes) or basic OCR (tested Qwen, DeepSeek) on architectural plans, it fails instantly. Walls intersect, doors swings and dimension lines heavily occlude the actual structures.

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

The Backbone: Swin Transformer + Detectron2.

Model trained on 1024X1024 images, with RTX 4090

Inference: Inference on CPU <10s.

Output: Clean JSON

Demo Performance: 67.1% AP50 for instance segmentation masks, and a 38.2% AP across the strict 0.50:0.95 IoU thresholds.

Vector Clean-up (The JSON Payload): 3D engines don't want pixel masks; they want math. The pipeline passes the raw predictions through Shapely to run boolean unions on intersecting walls, outputting clean, mathematically sound 2D polygons in a structured JSON payload.

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

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