Case study · Real estate
Interior Design Tool
Room photographs restyled into design options buyers can compare in seconds.
- Product
- Interior Design Tool
- Service
- AI development, computer vision
- Sector
- Real estate and interiors
- Core library
- OpenCV
At a glance
What it detects and changes
Floor, wall and ceiling identified in an ordinary room photograph.
Existing floor tiles found and swapped for alternatives in place.
Visualised in the room, sized and shaped to the space rather than pasted on.
Comparison is the point. Options a buyer can flip between beat a mood board.
The brief
Interior decisions are made by people who cannot picture the result
Choosing flooring from a sample is an act of imagination. The customer is asked to project a ten-centimetre square across a whole room they are standing in, and then compare that projection against four others they have also imagined.
Most people cannot do it reliably, which is why interior decisions get deferred, second-guessed and returned. The information needed to decide exists; it just is not visible in a form anyone can weigh.
You cannot compare options you have to imagine one at a time.
How it works
From a photo to a comparison
Photograph
The user supplies an ordinary photograph of the room as it is.
Detect
Computer vision algorithms identify the interior elements: floor, wall and ceiling, including the existing tiles.
Replace
Floor tiles are swapped for alternatives, and rugs of various shapes and sizes are placed into the space.
Compare
Options sit side by side in the user's own room, which is the only comparison that settles anything.
The build
Detection before substitution
The substitution is straightforward once the hard part is done, and the hard part is detection: knowing which pixels are floor, which are wall, which are ceiling, and where the existing tile boundaries fall. Get that wrong and the replacement reads as a sticker.
Built on OpenCV with computer vision algorithms handling element detection, the tool works from an ordinary room photograph rather than requiring a scan, a depth sensor or a prepared environment. That constraint is what makes it usable in a showroom or a living room instead of a studio.
Wow Labz delivered it as an AI development engagement with computer vision implementation.
Stack
What it was built with
Questions
The tool, answered
What does the Interior Design Tool do?
It detects interior elements in a room photograph, including floor, wall and ceiling, then lets users replace floor tiles and visualise rugs of various shapes and sizes in the space.
What is it built with?
OpenCV and computer vision algorithms for the element detection that everything else depends on.
Does it need special equipment?
No. It works from an ordinary room photograph rather than a scan or a depth sensor, which is what makes it usable in a real room.
Why does detection matter more than the replacement?
Because a substitution is only convincing if the boundaries are right. Misjudged surfaces make a replaced floor read as a pasted image rather than the room.
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