In order to make up the drawbacks that convolutional neural networks lack the ability of spatial structured learning in RGB-D scene parsing

we propose spatial structured inference deep fusion networks (SSIDFNs) on the basis of deep learning

the embedded structural inference layer organically combines conditional random fields (CRFs) and spatial structured inference model

which is able to learn the three-dimensional spatial distributions of objects and three-dimensional spatial relationships among objects in a more comprehensive and accurate way.Furthermore

the feature fusion layer takes both advantages of deep belief networks and improved CRFs

which is able to achieve deep structured learning according to the comprehensive semantic information of objects and semantic correlation in formation among objects.The experimental results demonstrate that the proposed SSIDFNs achieve the best mean accuracy 53.8% and 54.6% on the standard RGB-D datasets NYUDv2 and SUNRGBD respectively

which will be helpful to implement intelligent computer vision tasks

such as robot task planning and self-driving cars.