  In this paper, we propose a neural network architecture for scale-invariant
semantic segmentation using RGB-D images. We utilize depth information as an
additional modality apart from color images only. Especially in an outdoor
scene which consists of different scale objects due to the distance of the
objects from the camera. The near distance objects consist of significantly
more pixels than the far ones. We propose to incorporate depth information to
the RGB data for pixel-wise semantic segmentation to address the different
scale objects in an outdoor scene. We adapt to a well-known
DeepLab-v2(ResNet-101) model as our RGB baseline. Depth images are passed
separately as an additional input with a distinct branch. The intermediate
feature maps of both color and depth image branch are fused using a novel
fusion block. Our model is compact and can be easily applied to the other RGB
model. We perform extensive qualitative and quantitative evaluation on a
challenging dataset Cityscapes. The results obtained are comparable to the
state-of-the-art. Additionally, we evaluated our model on a self-recorded real
dataset. For the shake of extended evaluation of a driving scene with ground
truth we generated a synthetic dataset using popular vehicle simulation project
CARLA. The results obtained from the real and synthetic dataset shows the
effectiveness of our approach.
