Interior Object Detection and Color Harmonization

TL;DR

This paper presents an AI-based tool that detects room furniture, predicts suitable wall colors, and visualizes them to simplify interior decoration and color harmony decisions.

Contribution

It introduces a novel AI system combining object detection and deep learning to recommend and visualize wall colors based on room attributes and furniture.

Findings

Accurately detects furniture objects using YOLO with transfer learning.

Predicts wall colors that harmonize with room furniture and attributes.

Provides visually appealing color visualizations on room images.

Abstract

Confused about renovating your space? Choosing the perfect color for your walls is always a challenging task. One does rounds of color consultation and several patch tests. This paper proposes an AI tool to pitch paint based on attributes of your room and other furniture, and visualize it on your walls. It makes the color selection process easy. It takes in images of a room, detects furniture objects using YOLO object detection. Once these objects have been detected, the tool picks out color of the object. Later this object specific information gets appended to the room attributes (room_type, room_size, preferred_tone, etc) and a deep neural net is trained to make predictions for color/texture/wallpaper for the walls. Finally, these predictions are visualized on the walls from the images provided. The idea is to take the knowledge of a color consultant and pitch colors that suit the walls and provide a good contrast with the furniture and harmonize with different colors in the room. Transfer learning for YOLO object detection from the COCO dataset was used as a starting point and the weights were later fine-tuned by training on additional images. The model was trained on 1000 records listing the room and furniture attributes, to predict colors. Given the room image, this method finds the best color scheme for the walls. These predictions are then visualized on the walls in the image using image segmentation. The results are visually appealing and automatically enhance the color look-and-feel.