Semantic segmentation has made encouraging progress due to the success of
deep convolutional networks in recent years. Meanwhile, depth sensors become
prevalent nowadays, so depth maps can be acquired more easily. However, there
are few studies that focus on the RGB-D semantic segmentation task. Exploiting
the depth information effectiveness to improve performance is a challenge. In
this paper, we propose a novel solution named LDFNet, which incorporates
Luminance, Depth and Color information by a fusion-based network. It includes a
sub-network to process depth maps and employs luminance images to assist the
depth information in processes. LDFNet outperforms the other state-of-art
systems on the Cityscapes dataset, and its inference speed is faster than most
of the existing networks. The experimental results show the effectiveness of
the proposed multi-modal fusion network and its potential for practical
applications.

本文提出了一种名为 LDFNet 的新型解决方案，通过融合网络来综合利用亮度，深度和颜色信息，以提高 RGB-D 语义分割任务的性能，实验表明其潜力和适用性