  To improve segmentation performance, a novel neural network architecture
(termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional
neural network (DFCN) with a depth-sensitive fully-connected conditional random
field (DCRF). First, a DFCN architecture which fuses depth information into the
early layers and applies dilated convolution for later contextual reasoning is
designed. Then, a depth-sensitive fully-connected conditional random field
(DCRF) is proposed and combined with the previous DFCN to refine the
preliminary result. Comparative experiments show that the proposed DFCN-DCRF
has the best performance compared with most state-of-the-art methods.
