Dual Graphical Models for Relational Modeling of Indoor Object Categories

Lin Guo, Guoliang Fan, Weihua Sheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

There are three levels for indoor scene understanding, pixel level labeling, object level recognition and scene level holistic understanding. The three levels provide complementary bottom-up scene representation. Traditional research often addresses these three tasks separately where the three levels of semantic data are seldom jointly considered. We propose a new method to bridge the three semantic levels by using dual graphical models for relational modeling of object categories in indoor scenes. The vertical placement model captures top-down object configuration by which the visible pixels of some accessory objects could be used to infer the presence of a supportive object underneath. The horizontal placement model reveals how multiple object categories are related to each other on the ground in different indoor scenes. The experimental results show improvements on the bounding box accuracy using both vertical and horizontal placement models from pixel level labeling.