Semantic Segmentation Leveraging Simultaneous Depth Estimation.

Area of Science:

• Computer Vision
• Deep Learning
• Image Segmentation

Background:

• Semantic segmentation is crucial for various computer vision applications.
• Traditional Convolutional Neural Networks (CNNs) struggle with object geometry due to lack of depth information.
• This limitation can result in suboptimal segmentation quality.

Purpose of the Study:

• To improve semantic segmentation quality by incorporating depth estimation into CNNs.
• To develop a method that leverages depth information to better understand object distribution and geometric relationships.
• To enhance the performance of existing semantic segmentation networks.

Main Methods:

• Estimating depth information from RGB images using a dedicated depth estimation network.
• Feeding the estimated depth map into a CNN to guide the semantic segmentation process.
• Constructing a multi-branch encoder-decoder network for simultaneous processing of RGB and depth features.
• Step-by-step fusion of RGB and depth features within the network.

Main Results:

• The proposed method significantly enhances semantic segmentation quality.
• Experimental evaluations on four baseline networks demonstrate considerable improvements.
• The approach achieves better performance compared to traditional semantic segmentation networks without depth integration.

Conclusions:

• Integrating depth estimation with CNNs is an effective strategy for improving semantic segmentation.
• The proposed multi-branch network effectively fuses RGB and depth information for enhanced feature learning.
• This method offers a promising direction for advancing semantic segmentation accuracy in computer vision.