How to Convert a JPG Floor Plan Photo to a 3D Model

Clear, well-lit photos with legible wall lines produce the best results. The floor plan should fill most of the frame, and the image should be in focus. Higher resolution images give the AI more detail to work with.

Hand-drawn floor plans are not currently supported. The AI works best with clean digital floor plans, scanned architectural drawings, and PDF exports from design software. For accurate results, use professionally produced plans with straight, consistent wall lines.

Ritn3D supports JPG and PNG image formats. If your floor plan is in a different format like TIFF or BMP, you can convert it to JPG or PNG using any image viewer before uploading.

For best results, photograph the floor plan straight on to minimize perspective distortion. The AI can handle moderate angles, but a top-down photo of the plan produces the most accurate wall detection.

There is no strict minimum, but images above 800 pixels on the shortest edge work best. Very large images are automatically downscaled during processing. Standard phone camera photos are well within the ideal range.

Yes. Screenshots or saved images of floor plans from real estate websites work well as input. Ensure the image is clear and the walls are visible. Marketing overlays or heavy branding may require minor corrections in the review step.

Accuracy on phone-camera photos depends on three factors: the angle the photo was taken at, the resolution, and whether the wall lines are clean and uninterrupted. A straight-on phone-camera photo of a printed floor plan, taken under normal indoor lighting, detects walls, doors, and windows correctly on the first pass for roughly 80% of inputs in our internal benchmarks — most users only need to confirm the result in the review step. That is comparable to a scanned blueprint and within 10–15% of an architectural PDF exported directly from AutoCAD, Revit, ArchiCAD, or SketchUp, where wall lines are mathematically precise. Sharply angled photos, glare on glossy paper, or partial crops where wall lines are cut off introduce more noise. Whatever the AI gets wrong, the review step lets you fix walls, doors, and windows before the 3D model is generated — you are not stuck with the first detection.

Architectural PDFs from CAD software win on raw accuracy because the wall lines are stored as mathematically precise vectors — the AI does not have to infer where the wall edge is. In our internal benchmarks, vector PDFs detect correctly on the first pass roughly 90–95% of the time, scanned blueprints around 80%, and phone-camera JPGs of printed plans around 75–85% depending on lighting and angle. The accuracy gap closes in the review step where any user can drag wall endpoints, add or delete walls, and reposition doors and windows. For most homeowners, real estate agents, and designers, a phone photo of a printed plan is the most convenient input and produces a usable 3D model with no edits or one or two minor edits.

Both work. The detection treats walls as individual line segments rather than assuming right angles, so angled walls, octagonal rooms, alcoves, and open-plan layouts where one room flows into another are all handled. For open-plan spaces where there's no actual wall between two rooms, you can decide in the review step whether to treat them as one large room or two — the choice affects how the 3D model labels and lights the space.