融合深度图像的卷积神经网络语义分割方法

该文提出了一种基于深度学习框架的图像语义分割方法，通过使用由相对深度点对标注训练的网络模型，实现了基于彩色图像的深度图像预测，并将其与原彩色图像共同输入到包含带孔卷积的全卷积神经网络中。考虑到彩色图像与深度图像作为物体不同的属性表征，在特征图上用合并连接操 作而非传统的相加操作对其进行融合，为后续卷积层提供特征图输入时保持了两种表征的差异。在两个数据集上的实验结果表明，该法可以有效提升语义分割的性能。

Depth-Aware Convolutional Neural Networks for Semantic Segmentation

In this paper, a deep learning-based image semantic segmentation method was studied. A neural network trained by point pair annotations of relative depth was used to predict depth images from common color images. By feeding the color and depth images into a fully convolutional networks with atrous convolution, accurate segmentation of the images could be obtained. As different representations of object properties, concatenate operation on the feature maps instead of traditional adding operation was used to fuse them. The differences between these two representations could be preserved when they were feed into the next convolutional layers. Experimental results on two different datasets show that, performance of semantic segmentation can be improved by the proposed method.