Kitchen Surface Segmentation - Wortel AI annotation portfolio

Cabinet runs, worktop and splashback each cut as separate classes - annotated — Cabinet runs, worktop and splashback each cut as separate classes

Cabinet runs, worktop and splashback each cut as separate classes - source — Cabinet runs, worktop and splashback each cut as separate classes

Open-plan kitchen with the island separated from the perimeter run - annotated — Open-plan kitchen with the island separated from the perimeter run

Open-plan kitchen with the island separated from the perimeter run - source — Open-plan kitchen with the island separated from the perimeter run

Glazed wall units traced door by door - annotated — Glazed wall units traced door by door

Glazed wall units traced door by door - source — Glazed wall units traced door by door

Challenges

Glossy cabinet doors and polished worktops reflect the room, hiding the true edge.
Wide-angle listing photography distorts straight edges towards the frame corners.
Cabinet seams are often only a few pixels wide at typical listing resolution.
Under-cabinet shadow makes the worktop-to-cabinet boundary ambiguous.
Open-plan rooms mix kitchen and living surfaces that must not share a class.
Appliances set flush into cabinetry need a boundary that follows the trim, not the door.

Project overview

Interior listing photographs were segmented surface by surface so that a downstream renderer could recolour or replace any single element without touching the rest of the room. Each cabinet run, worktop, splashback, appliance and floor plane received its own polygon and class.

The work sits behind a virtual restyling product, so boundary precision matters more than it would for a detection dataset. A worktop mask that bleeds a few pixels onto the cabinet below produces a visible halo the moment the surface is recoloured.

Dataset