Robust object detection by cuboid matching with local plane optimization in indoor RGB-D images

Abstract

We propose a new cuboid matching algorithm for robust object detection in RGB-D images from an indoor scene. Unlike traditional bounding boxes, a cuboid is more flexible and accurate to represent the object's orientation and basic geometry that can serve as an informative mid-level representation for scene understanding. However, over-detection and miss-detection are common problems when the scene is too cluttered and has many irrelevant planar surfaces. We approach these problems from two perspectives. First, we apply a few planar features to improve initial plane generation and to select dominant plane candidates for cuboid initialization. Second, cuboid candidates are optimized to their local fitness involving both color and depth features. The experimental results show significant improvements over the state-of-art method both quantitatively and qualitatively.